Technical Support Document (TSD) for 

Carbon Pollution Guidelines for Existing Power Plants: 

Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: 

Electric Utility Generating Units 

Docket ID No. EPA-HQ-OAR-2013-0602 

Greenhouse Gas Mitigation Measures 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

August 3, 2015 
TABLE OF CONTENTS 

Chapter 1: Introduction ......................................................................................................................1-1 

Chapter 2: Heat Rate Improvement at Existing Coal-fired Electric Generating Units ......................2-1 

Chapter 3: Generation Shifts and CO2 Reduction amongst Affected Electric Generating Units ......3-1 

Chapter 4: Cleaner Generation Sources .............................................................................................4-1 

Chapter 5: Carbon Capture & Storage ...............................................................................................5-1 
1 – 1 

CHAPTER 1: INTRODUCTION 

Under the authority of Clean Air Act (CAA) section 111(d), EPA is establishing final CO2

emission guidelines for existing fossil fuel-fired electric generating units (EGUs) – the Clean 

Power Plan (CPP). More specifically, EPA is establishing: (1) carbon dioxide (CO2) emission 

performance rates based on the best system of emission reduction (BSER) for two subcategories 

of existing fossil fuel-fired EGUs, fossil fuel-fired electric utility steam generating units and 

stationary combustion turbines, and (2) guidelines for the development, submittal and 

implementation of state plans that implement the CO2 emission performance rates. The emission 

performance rates reflect the “best system of emission reductions ... adequately demonstrated” for 

CO2 emissions from the EGU source category. The guidelines also provide for the development, 

submittal and implementation of state plans that implement the CO2 emission performance rates, 

either directly by means of source-specific requirements or through measures that achieve 

equivalent CO2 reductions from the same group of EGUs. This technical support document (TSD) 

provides additional information, data, and analysis to support EPA’s assessment and application 

of BSER. 

In the June 2014 proposal, EPA proposed to determine that the best system of emission reduction 

adequately demonstrated for reducing CO2 emissions from existing EGUs was a combination of 

measures: (1) increasing the operational efficiency of existing coal-fired steam EGUs, (2) 

substituting increased generation at existing NGCC units for generation at existing steam EGUs, 

(3) substituting generation from low- and zero-carbon generating capacity for generation at 

existing fossil fuel-fired EGUs, and (4) increasing demand-side energy efficiency to reduce the 

amount of fossil fuel-fired generation. These activities were categorized as four “building blocks.” 

The final rule BSER incorporates certain changes from the proposed rule, reflecting EPA’s 

consideration of comments received and further analysis. The principal changes are the exclusion 

from the BSER of emission reductions achievable through demand-side energy efficiency 

(building block 4) and through nuclear generation; a revised approach to determination of emission 

reductions achievable through increased renewable energy generation; a consistent approach to 

determination of emission reductions achievable through all the building blocks that better reflects 

the regional nature of the electricity system; and a revised interim goal period of 2022 to 2029 

(instead of the proposed interim period of 2020 to 2029). These changes to the BSER and the 

building blocks are discussed in more detail in the preamble for the CPP Final Rule. Further 

information can be found in the Legal Memorandum for the CPP Final Rule, the CO2 Emission 

Performance Rate and Goal Computation TSD for the CPP Final Rule, and the Response to 

Comments document, all of which are available in the docket. 
1 – 2 

This TSD includes chapters with detailed information and EPA’s evaluation of each building block 

included in BSER, and also includes a chapter on carbon capture and storage technology (CCS).1

A separate TSD includes supplemental information on demand-side energy efficiency (Demandside Energy Efficiency TSD for CPP Final Rule), which is not part of BSER, but supports the 

Regulatory Impact Analysis. 

While evaluating each measure, EPA considered the technical feasibility, applicability and use, 

application level appropriate for BSER, and cost effectiveness associated with reducing GHG 

emissions at EGUs. The application of the building blocks as BSER is consistent with current 

trends in the electric power sector and with strategies that companies and states are already taking 

to reduce GHG emissions. The application of the building blocks as BSER supports achieving 

cost-effective and technically feasible reductions of CO2, consistent with the final CPP rule. 

 

1

 The proposed rule GHG Mitigation Measures TSD (docket ID #EPA-HQ-OAR-2013-0602-0437) also included a 

chapter on fuel switching at affected EGUs. This mitigation strategy was not included as BSER in the proposal, nor 

is it included in the final rule. 
2 – 1 

CHAPTER 2: CO2 INTENSITY REDUCTIONS THROUGH HEAT RATE 

IMPROVEMENTS AT EXISTING COAL FIRED ELECTRICITY 

GENERATING UNITS 

This is Technical Support Document (TSD) incorporates by reference the materials found in the 

Clarified Technical Support Document (TSD) for Carbon Pollution Guidelines for Existing Power 

Plants: Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: 

Electric Utility Generating Units: GHG Abatement Measures as well as the Dataset related to 

Chapter 2 of the GHG Abatement Measures TSD, including inter alia the description of the data 

and methodology, analytical results, illustrative equipment upgrades, costs, and datasets.1

2.0 Overview 

The heat rate of coal-fired electricity generating units (EGUs) is the amount of energy required to 

generate each kilowatt-hour (kWh) of electricity. The more efficiently an EGU converts the energy 

embedded in coal into electricity, the lower the EGU’s heat rate and, correspondingly, the lower 

its CO2 intensity (CO2 lb/kWh). Because heat rate improvements reduce fuel consumption, 

improving coal-fired EGUs’ heat rates can be a cost-effective method of reducing the CO2 intensity 

of the power system as a whole.2 In addition, it can provide additional economic benefits from 

reduced fuel costs. 

To assess the potential heat rate improvements of existing coal-fired EGUs, EPA analyzed 

historical gross heat rate data from 2002 to 2012 for 884 coal-fired EGUs that reported both heat 

input and gross electricity output to the agency in 2012. The agency grouped the EGUs by regional 

interconnections – Western, Texas, and Eastern – and analyzed potential heat rate improvements 

within each interconnection using three analytical approaches. The approaches compare EGUs’ 

“best” or “benchmark” historical gross heat rates against EGUs’ 2012 gross heat rate. By founding 

the methodology on comparisons between EGUs’ historical and baseline performances, the 

analyses control for variations in EGU design and largely obviate the need for subcategorization. 

By using the 2012 gross heat rate as the baseline, the analyses account for actions that individual 

EGUs have already taken to improve and maintain gross heat rate. Accordingly, the results identify 

the overall heat rate improvements that will result if coal-fired EGUs simply returned to their 

demonstrated and achievable past performance. 

 

1

 See Clarified Technical Support Document (TSD) for Carbon Pollution Guidelines for Existing Power Plants: 

Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generating 

Units; GHG Abatement Measures, Docket No. EPA-HQ-OAR-2013-0602-17180 (Sept. 16, 2014), available at 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2013-0602-17180; and Dataset related to Chapter 2 

of the GHG Abatement Measures TSD, Docket No. EPA-HQ-OAR-2013-0602-0238 (June 8, 2014), available at 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2013-0602-0238. 

2

 Improving heat rate can result in a “rebound effect” that undermines the emissions reductions a coal-fired EGU 

would otherwise achieve. For a detailed discussion of the consequences of the rebound effect for building block 1 and 

the best system of emission reductions, see sections V.A and V.C of the final rule preamble of the CPP. 
2 – 2 

In the first approach, “best historical average performance,” the agency selected each EGU’s best 

(i.e., lowest) 1- and moving 2-year gross heat rates, calculated the generation weighted average 

across all units, and compared that to the generation-weighted 2012 average gross heat rate across 

all units. For the second and third approach, the agency controlled for select conditions beyond the 

EGU operator’s control – hourly capacity factor and hourly ambient air temperature. These two 

conditions account for approximately 26 percent of variation in gross heat rate for the 884 coalfired EGUs between 2002 and 2012. To control for these conditions, the agency grouped, or 

“binned,” data by hours with similar hourly capacity factors (using 10 percent increments) and 

hourly ambient air temperature conditions (using 10° F ranges). In the second approach, “best 

historical average performance under similar conditions,” within each capacity-temperature bin 

for each coal-fired EGU, EPA calculated the bin-specific 1- and moving 2-year gross heat rates, 

selected the lowest 1- and moving 2-year gross heat rate within that capacity-temperature bin, 

calculated the generation weighted average across all bins and units, and compared that to 

the generation weighted 2012 average gross heat rate. For the third approach, “efficiency and 

consistency improvements under similar conditions,” the agency identified “benchmarks” for each 

EGU equal to the EGU’s lowest hourly gross heat rate (characterized as the 10th percentile value 

over 1- and 2-year periods) for each capacity-temperature bin.3

 Then the agency determined the 

heat rate that could be achieved if the EGU implemented measures to operate more consistently 

nearer the lowest “benchmark,” and compared that to the actual gross heat rate. 

The results of these three approaches indicate that there is significant potential for heat rate 

improvement ranging from 4.0 to 6.6 percent nationally if coal-fired EGUs return to their best past 

performance between 2002 to 2012. The most conservative approach that is supported by the 

weight of evidence yields average potential heat rate improvements of 2.1 percent in the Western 

Interconnection, 2.3 percent in the Texas Interconnection, and 4.3 percent in the Eastern 

Interconnection. 

2.1 Introduction 

Heat rate, a measure of efficiency for fossil fuel-fired EGUs, is commonly expressed as the amount 

of heat input in British thermal units (Btu) required to generate one kWh of electricity (Btu/kWh). 

As a coal-fired EGU’s heat rate improves (i.e., efficiency improves), less coal or other fuel is 

required to produce the same amount of electricity. Because less fuel is combusted, the amount of 

CO2 and other emissions (e.g., mercury, NOX, PM, SO2) released to the environment also declines. 

In addition to the clear environmental benefits of heat rate improvements, there are important 

 

3

 The third approach uses hourly values to determine a heat rate benchmark for each EGU for periods with similar 

hourly capacity factor and hourly ambient temperature conditions. Therefore, the agency determined it was appropriate 

to select the 10th percentile value to (1) control for unusually low hourly values and (2) set a benchmark with sufficient 

hourly measurements at or below the benchmark. The first and second approaches use annual averages so the agency 

did not determine it necessary to remove unusually low or unusually high heat rate values as the low and high values 

will generally balance out. 
2 – 3 

economic co-benefits attributable to reduced fuel costs and coal combustion residue management 

and disposal costs. 

Heat rates of existing coal-fired EGUs in the U.S. vary substantially. The variation in heat rates 

among EGUs with similar design characteristics (e.g., boiler type, fuel type, size, cooling system) 

as well as year-to-year variation in heat rate at individual EGUs indicate that there is potential for

efficiency improvements that can yield significant CO2 emission reductions and fuel savings for 

the existing coal-fired EGU fleet as a whole. This chapter presents (1) the characteristics of existing 

coal-fired EGUs in the US, including heat rate performance over time, (2) the impact that design 

characteristics and other factors may have on EGU heat rates, (3) additional equipment upgrades 

and practices that can improve efficiency at existing coal-fired EGUs, (4) the potential heat rate 

improvements from existing coal-fired EGUs that, when realized, would reduce CO2 emission 

intensity, and (5) responses to key comments about building block 1 of the proposed rule. 

2.2 Coal-fired EGUs 

Several different coal combustion configurations and technologies are used at existing coal-fired 

EGUs in the U.S. Nevertheless, they all commonly follow the same process for converting the 

energy stored within coal into electricity. Figure 2-1 depicts a pulverized coal-fired EGU, the most 

widely used configuration in the U.S.4 In general, coal is ground into small pieces or a fine powder, 

and then fed into a furnace where it is burned. The heat from coal combustion boils preheated 

water creating high-pressure high-temperature steam that is transported to a steam turbinegenerator where the steam passes through, forcing the blades of the steam turbine to rotate, which 

in turn drives a generator to generate electricity. The steam drops in pressure and expands in 

volume as its energy is transferred to the turbine. After the steam has expanded, the exhaust steam 

is cooled in a heat exchanger (i.e., condenser). The water is then returned to the boiler to repeat 

the cycle. Coal combustion waste gases (i.e., flue gas), including various air emissions (e.g., CO2, 

mercury, NOX, PM, SO2), are drawn downstream from the furnace by fans and pass through 

pollution control devices before they are released from the stack to the atmosphere. 

 

4

 For information about coal combustion configurations at coal-fired EGUs, see supra note 1. 
2 – 4 

Figure 2-1 Coal-fired EGU schematic 

Note: Used by permission. Courtesy of the Babcock & Wilcox Company. 

2.2.1 Design characteristics and site-specific factors that affect heat rate 

A variety of design characteristics, site-specific factors, and operating conditions can affect the 

heat rate of existing coal-fired EGUs. Some of these factors are influenced by or are under the 

direct control of the EGU operator, but other factors may be beyond the operator’s control, such 

as: 

Thermodynamic cycle. The thermodynamic steam cycle of a coal-fired EGU indicates the range of 

steam temperatures and pressures used in the boiler and steam turbine. There are two common 

steam cycles: subcritical and supercritical. In general, the higher the steam temperature and 

pressure, the higher the efficiency of the EGU and the lower the heat rate. Existing subcritical 

boilers that operate below the critical pressure of water (approximately 22.1 MPa or 3,206 psia) 

generally have higher heat rates with design thermal efficiencies of up to 35 percent while 

supercritical and ultrasupercritical EGUs that operate above the critical pressure of water may have 

design thermal efficiencies of 40 to 45 percent. In any case, actual thermal efficiencies in daily 

operation are usually lower than design values. 

Boiler and steam turbine size. In general as the size of the boiler and the steam turbine increase, 

heat rate decreases. Larger coal-fired EGUs generally have lower heat rates for technical and 

economic reasons. Furthermore, relative to smaller EGUs, the larger EGUs typically have lower 

percentages of draft loss, heat loss, and seal leakage, and also tend to use more advanced 
2 – 5 

technologies. They also have economies of scale for investment, operation, and maintenance, and 

have higher capacity factors because they are often dispatched before smaller, less-efficient units. 

Cooling system. The design of the cooling system for existing coal-fired EGUs – once-through, 

recirculating (i.e., evaporative), or dry – is largely dependent on an EGU’s proximity to water 

resources, ecological concerns, and regulatory requirements. The cooling system design directly 

affects the steam turbine’s ability to extract energy from the steam and affects overall efficiency. 

The amount of energy that the steam turbine can extract from the steam depends on the steam 

temperature and pressure as well as condenser pressure (affected by the cooling system design, 

condenser cleanliness, EGU load, and ambient cooling water or air temperature). At lower 

condenser pressures (within design limits), more energy can be extracted by the steam turbine, 

with a resulting lower EGU heat rate. 

Auxiliary equipment. Auxiliary equipment, such as pumps, fans, pulverizers, and pollution control 

devices, require power to run, thereby consuming some of the electricity generation on-site and 

reducing the amount of electricity output available for use on the electricity grid. These systems 

vary in efficiency based on design, age, and usage.

Geographic location. Ambient conditions (e.g., seasonal ambient temperatures, cooling water 

temperatures) at the power plant location affect an EGU’s heat rate. Ambient air is mixed with the 

fuel prior to combustion, and a fraction of energy released during combustion heats the incoming 

air, increasing heat rate. Cooler water temperatures improve the efficiency of the cooling system, 

thereby reducing heat rate. Colder cooling water (or ambient air for dry cooling systems) in the 

condenser reduces the back pressure in the condenser, allowing for more energy to be extracted by 

the steam turbine. 

Coal rank and quality. Higher rank coals (e.g., bituminous) tend to have higher energy contents 

(i.e., carbon content), burn more intensely, and have a lower moisture content than mid-rank (e.g.,

subbituminous) and low-rank (e.g., lignite) coals. In general, the lower the moisture content of 

coal, the less energy that is needed to evaporate the moisture and, therefore, the more energy that 

is available for producing steam, thus lowering heat rate. The ash content of the coal can also affect

heat rate by accumulating on heat transfer surfaces (i.e., metallic tubes) and impeding energy 

transfer to the steam. While systems are designed to remove this “soot,” the quantity present 

between surface cleanings hinders energy transfer and results in higher heat rates. 

2.2.2 Regional electricity interconnections 

As discussed in sections V.A and V.C of the preamble for the final CPP rule and the associated 

CO2 Emission Performance Rate and Goal Computation Technical Support Document for CPP 

Final Rule, EPA assessed the potential CO2 reductions achievable through each of the building

blocks that comprise the best system of emission reduction. The agency conducted assessments at 

the level of the three large regional electricity interconnections: Western, Texas, and Eastern (see 
2 – 6 

Figure 2-2). The Western Interconnection is a major power grid that comprises one North 

American Electric Reliability Corporation (NERC) region – the Western Electricity Coordinating 

Council (WECC). The Western Interconnection encompasses all or portions of 14 western states. 

The Texas Interconnection includes the portion of Texas managed by the Electric Reliability 

Council of Texas (ERCOT). The Eastern Interconnection is the other major regional power grid, 

comprising six NERC regions – Florida Reliability Coordinating Council (FRCC), Midwest 

Reliability Organization (MRO), Northeast Power Coordinating Council (NPCC), Reliability First 

Corporation (RFC), SERC Reliability Corporation (SERC), and Southwest Power Pool (SPP) – 

covering all or portions of 39 eastern and midwestern states. 

Each interconnection is a highly-connected electricity grid in which, during normal conditions, 

power generated in one part of the interconnection can help balance power generation and load in 

all other parts of the system. Therefore, each interconnection must carefully manage electricity 

generation and load to ensure safe and reliable electricity service. 

For the proposal, EPA analyzed the potential heat rate improvements from coal-fired EGUs at the 

national level. Some commenters suggested that EPA should adjust the analysis and develop statespecific estimates of potential heat rate improvements from coal-fired EGUs. While the agency 

carefully evaluated these comments, the agency determined that due to the highly interconnected 

nature of the electricity system, and as further discussed in section V.C.3 of the final rule preamble, 

it is more appropriate in this rule to assess potential for heat rate improvements from existing coalfired EGUs at the regional interconnection level than at the national or state level. 

2.2.3 Coal-fired EGUs in operation in 2012 

In 2012, there were 884 coal-fired EGUs at 367 power plants in the contiguous U.S. (CONUS) 

that reported hourly heat input and gross electricity generation to EPA’s Clean Air Markets 

Division.5

 These EGUs are spread across the CONUS (see Figure 2-2) in each of the three regional 

electricity interconnects. Of the 884 coal-fired EGUs, 93 (10.5 percent) are located in the Western 

Interconnection, 32 (3.5 percent) are located in the Texas Interconnection, and 759 (86 percent) 

are located in the Eastern Interconnection. 

 

5

 Excludes cogeneration, integrated gasification and combined cycle (IGCC), retired, and non-operational EGUs. 

EGUs that reported steam co-generation to EPA or EIA are excluded because only a portion of heat input is used to 

generate electricity while the remainder is used to generate steam, making it difficult to meaningfully compare these 

EGUs’ heat rates. IGCC units are excluded because they are not conventional steam boilers and may regularly use 

pipeline natural gas in lieu of syngas from coal gasification. EGUs retired prior to 2012 or EGUs that did not operate 

in 2012 are also excluded because they do not have 2012 operating data necessary to estimate potential heat rate 

improvement using the methodology described later in this document. 
2 – 7 

Figure 2-2 Power plants with coal-fired EGUs reporting heat input 

and gross electricity generation in 2012 

The population of 884 coal-fired EGUs includes units with a variety of design configurations and 

operating characteristics that can affect an EGU’s heat rate, such as thermodynamic steam cycle, 

coal rank and coal quality, boiler type, boiler size (i.e., maximum hourly heat input), and generator 

power output capacity. 

At the end of 2012, the age of the 884 coal-fired EGUs varied widely, ranging from less than 1 

year to 68 years. Nationally, the average age was 42 years, but this differed among the 

interconnections. The Texas Interconnection had the lowest average age of existing coal-fired 

EGUs at 27 years followed by the Western Interconnection at 34 years. The average age of the 

coal-fired EGUs in the Eastern Interconnection was 44 years (see Figure 2-3). 

Similarly, electricity generating capacity of the 884 coal-fired EGUs varied widely within and 

between the interconnections. In 2012, the capacity ranged from 25 MW to nearly 1,500 MW with 

a national average capacity of 385 MW.6

 In the Western Interconnection the average generator 

capacity of coal-fired EGUs was 390 MW, in the Texas Interconnection the average was 689 MW, 

and in the Eastern Interconnection the average was 372 MW (see Figure 2-4). 

 

6

 EGU operators report maximum hourly load data to EPA/CAMD. This value may differ from the EGU’s nameplate 

capacity and summer net capacity. 
2 – 8 

Figure 2-3 Age (years) distribution of coal-fired EGUs in 2012, by interconnection 

Figure 2-4 Generator capacity (MW) distribution of coal-fired EGUs in 2012, by 

interconnection 
2 – 9 

2.2.3.1 Historical gross heat rate trends of coal-fired EGUs 

Between 2002 and 2012, the average gross heat rates of the 884 coal-fired EGUs have fluctuated 

from year to year. Nationally, the average gross heat rate7

 declined between 2002 and 2008 to a 

low of 9,645 Btu/kWh in 2008 and 2009 (see Figure 2-5). However, after 2009, the average annual 

gross heat rate of the 884 coal-fired EGUs began to deteriorate, climbing to 9,753 Btu/kWh in 

2012. 

The variation for each of the interconnection regions was more pronounced. The average annual 

gross heat rate of the 93 coal-fired EGUs in the Western Interconnection declined from nearly 

10,458 Btu/kWh in 2002 to 9,888 Btu/kWh in 2012, just slightly higher than the lowest gross heat 

rate in 2010. The larger and relatively younger 32 coal-fired EGUs in the Texas Interconnection 

saw less change than those in the Western Interconnection. The lowest average gross heat rate for 

the Texas Interconnection occurred in 2009 and was only slightly higher in 2012 (9,760 Btu/kWh 

and 9,789 Btu/kWh in 2009 and 2012, respectively). In the Eastern Interconnection, where 86 

percent of the coal-fired EGUs are located, gross heat rates have steadily increased since 2008, 

rising from 9,567 Btu/kWh to 9,700 Btu/kWh. 

Figure 2-5 Gross heat rate trends of coal-fired EGUs, 2002-2012 by interconnection 

Notes: red dotted line represents the 11-year arithmetic average; 

axis does not start at zero to better illustrate trends in gross heat rate

Commenters have offered a variety of opinions in an attempt to explain these gross heat rate trends 

over time, such as shifts from low-moisture bituminous coals to higher-moisture subbituminous 

coals from the Powder River Basin, installation of air pollution control devices in response to 

 

7

 EPA used hourly heat input (Btu) and hourly gross electricity generation (kWh) to calculate the hourly gross heat 

rate (Btu/kWh). As described later in this chapter, gross generation was the only complete and accurate hourly dataset 

available to EPA. Therefore, unless otherwise noted, references to heat rate in this chapter refer to gross heat rate. 
2 – 10 

federal and state regulations, retirements of less-efficient coal-fired EGUs, fluctuations in 

precipitation and weather conditions, deterioration of plant equipment or deferral of equipment 

maintenance, or changes in dispatch patterns and capacity factors. EPA acknowledges that there 

are many factors that can affect the heat rate of coal-fired EGUs, some of which are within the 

EGU operator’s control and some that are not. In the analysis of potential heat rate improvements 

described below, the agency attempts to limit the influence of some of the factors outside the EGU 

operator’s control.

2.3 Heat rate improvement practices and equipment upgrades at coal-fired EGUs 

In the proposal TSD, EPA provided examples of best practices and equipment upgrades that may 

be available to existing coal-fired EGUs to improve heat rate and, as a result, reduce the EGU’s 

CO2 emissions intensity. 8

 Several commenters also provided extensive information about 

additional best practices and equipment upgrades that may be available for coal-fired EGUs to 

improve heat rate. Commenters also noted that many coal-fired EGUs have already implemented 

heat rate improvement measures for economic reasons and requiring EGUs to implement the 

measures again might not yield substantial benefits or might not be feasible. 

As explained in section 2.5 below and in preamble sections V.A and V.C, the agency has decided 

not to estimate a separate potential for heat rate improvements from equipment upgrades. As stated 

in the proposal TSD, the agency lacks reliable and complete information about the unit-level 

upgrades that coal-fired EGUs have already implemented and it is not practical for EPA to conduct 

engineering analyses at each coal-fired EGU for the purpose of answering the question about 

whether existing coal-fired EGUs are already taking advantage of all opportunities to improve heat 

rate. Nonetheless, the agency remains confident that there are additional opportunities for coalfired EGUs to achieve heat rate improvements by implementing some combination of best 

practices and equipment upgrades, including the illustrative best practices and equipment upgrades 

shown below. The agency recognizes that the types and extent of capital improvements and 

operational and maintenance improvements available to individual coal-fired EGUs will vary. For 

this and other reasons stated below, the agency is therefore not basing the estimate of potential 

heat rate improvement on a specific proportion of best practices or equipment upgrades. 

The lists below are intended to provide examples of equipment upgrades and best practices to 

improve heat rates. While the agency believes these equipment upgrades and best practices are 

highly cost-effective, the agency recognizes that some EGUs (e.g., small EGUs) may not find each 

of the measures to be cost effective. In addition, the agency recognizes that not all measures are 

applicable to each and every EGU. Therefore, these lists are intended only to demonstrate the types 

of actions that EGUs might be able to implement to improve or maintain heat rate. 

 

8

 See Clarified Technical Support Document, supra note 1. 
2 – 11 

2.3.1 Example equipment upgrades for heat rate improvement 

• Install intelligent sootblowing system 

• Replace feed water pump steam turbine seals 

• Overhaul high pressure feed water pumps 

• Upgrade main steam turbine seals 

• Upgrade steam turbine internals 

• Install variable frequency drives for motors 

• Retube or expand the condenser 

• Install sorbent injection system to reduce flue gas sulfuric acid to allow lower 

temperature exhaust gas 

• Upgrade air heater baskets for lower temperature operation 

• Upgrade and repair flue gas desulfurization systems

• Refurbish the economizer 

• Upgrade ESP components to lower auxiliary power consumption 

• Improve SCR and FGD system components to lower draft loss 

2.3.2 Example best practices for heat rate improvement 

• Adopt training for O&M staff on heat rate improvements 

• Perform on-site appraisals to identify areas for improved heat rate performance 

• Install neural network software for combustion/optimization with monitoring system for 

heat rate optimization 

• Repair steam and water leaks – replace leaking valves and steam traps 

• Replace / repair worn air heater seals 

• Manage feed water quality 

• Chemical clean boiler to remove scale build-up from water side 

• Install and operate condenser tube cleaning system 

• Repair boiler furnace and ductwork cracks to prevent boiler air in-leakage 

• Clean air preheater coils to restore performance 

• Adopt sliding pressure operation to reduce turbine throttling losses 

• Reduce activation of attemperator which compensates for over-firing the unit 

• Remove deposits on turbine blades 

2.3.3 Heat rate improvement measures identified by external studies 

There are a number of engineering studies that explore opportunities for improving the efficiency 

of existing coal-fired power plants, including studies by Sargent & Lundy and NETL that were 

referenced in the proposal TSD. Some of the practices and equipment upgrades have the potential 

to improve gross heat rate while others will not affect an EGU’s gross heat rate, but they will 
2 – 12 

reduce the auxiliary load from on-site equipment, thereby improving the net heat rate (i.e., making 

more net electricity available for the same amount of fuel input). The following potential efficiency 

improvements in the boiler, turbine, flue gas system, air pollution control equipment, and the water 

treatment system are discussed below.9

2.3.3.1 Boiler systems 

2.3.3.1.1 Materials handling 

Materials handling includes the handling and transport of coal, the preparation of coal for 

combustion, and handling of combustion residues: 

• Coal-handling equipment: Installation of energy-efficient motors and variable frequency 

drives can reduce auxiliary power requirements. 

• Coal pulverizers: Upgrading the pulverizers to provide more consistent size and finer coal 

particles can improve the EGU’s combustion efficiency, produce less unburned coal, and 

consequently reduce fuel cost and gross heat rate. 

• Bottom ash handling equipment: Switching from a water-sluicing bottom ash system to a 

dry system can reduce the auxiliary requirements and reduce the amount of wastewater to 

the water treatment plant. 

2.3.3.1.2 Boiler control system 

The boiler control system has a large impact on the gross heat rate of the unit. Commonly referred 

to as the DCS (Distributed Control System), this computer controls and displays many parameters 

affecting boiler operation. A neural network is an advanced control system utilizing artificial 

intelligence and installed in addition to the plant DCS. The neural network evaluates many 

parameters of the EGU’s operation and makes adaptive tuning based on statistical analysis to 

predict performance for increased efficiency. Many vendors offer neural network systems to 

improve the overall efficiency. These systems can offer gross heat rate improvements up to 150 

Btu/kWh. 

2.3.3.1.3 Steam turbine 

The steam turbine extracts energy from the steam and rotates a generator to produce electricity. 

Modern developments allow the steam turbine to extract more energy from the same amount of 

steam than was possible with equipment from decades ago. Replacing existing steam turbines with 

advanced designs or, where practical, upgrading turbines with improved internals can reduce gross 

heat rate by 100-300 Btu/kWh. As one commenter pointed out, Leyzerovich explains that 

 

9

 Chapter 2 of the proposal TSD provides a detailed description of numerous well-proven measures for improving heat 

rate at coal-fired EGUs. See supra note 1. 
2 – 13 

upgrading and retrofitting a turbine at a 20-year old 500 to 700 MW coal-fired EGU can reduce 

heat rate by a total of about 4 percent while also increasing output by approximately 5 percent.10

2.3.3.1.4 Feed water heaters 

Boiler feed water heaters pre-heat the water going to the boiler. The hotter the water entering the 

boiler, the less energy it takes to convert it to steam. Like any heat exchanger, EGUs can improve 

efficiency by removing built-up scale or increasing heat transfer surface area. Chemical cleaning 

removes scale and restores heat transfer efficiency. 

2.3.3.1.5 Condenser 

Condensers are subject to fouling and plugging, which directly impact the heat transfer efficiency 

and water quality. Closed cooling water systems can be used to provide better control over water 

quality and tube cleaning can be performed as needed. Condenser upgrades and maintenance 

typically improve gross heat rate by 30-70 Btu/kWh.

2.3.3.1.6 Cooling tower 

The temperature of cooling water affects condenser performance. Supplying cold water allows the 

condenser to operate at lower pressures, which allows the steam turbine to extract more energy for 

power generation. As the cooling towers foul, the water returned to the condenser becomes warmer 

and negatively affects steam turbine performance and reduces gross electricity generated. Routine 

maintenance on the cooling towers might include discarding accumulated debris, removing 

biologic growth, replacing defective fill material, ensuring uniform water distribution, correcting 

for uneven air distribution, verifying water flow rates, and replacing missing louvers 

2.3.3.1.7 Boiler feed water pumps 

Boiler feed pumps require a large amount of auxiliary power to pump large amounts of boiler feed 

water through the heaters and the boiler. Due to their continuous operation, the pumps wear over 

time, lose efficiency, and require more energy to move the same volume of water. A pump 

overhaul restores efficiency, ensures reliability, and can reduce the net heat rate by 25-50 Btu/kWh. 

2.3.3.1.8 Water Treatment System 

The quality of boiler water affects the rate of scale build-up on boiler tubes. Scale inhibits heat 

transfer and the EGU operator typically compensates by increasing the firing-rate to overcome this 

effect. Scale can reduce heat transfer up to 10 percent in severe cases. However, the higher the 

quality of water used in the boiler, the slower the scale build-up and thus the higher the efficiency 

 

10 See page 477 in AS Leyzerovich, 2007. Steam Turbines for Modern Fossil-Fuel Power Plants. CRC Press. ISBN 

13: 978-1420061024. 
2 – 14 

of the EGU. Enhanced water treatment can reduce scale. In addition, scale can be removed through 

chemical cleaning to restore heat transfer and lower gross heat rate. 

The cooling water system can also suffer from poor water quality for the same reason – scale buildup fouls the heat transfer surfaces in the condenser, causing higher condenser temperatures and 

reducing the capability of the turbine to extract energy from the steam. Chemical cleaning and 

better water quality can mitigate the issue. 

2.3.3.1.9 Variable frequency drives 

Replacing fixed speed electric motors with variable frequency drives reduces auxiliary power 

requirements by reducing the drives’ speed (i.e., rpm) when maximum speed is not required. The 

net heat rate improvements can be 10-150 Btu/kWh. 

2.3.3.2 Flue gas and emission control technologies 

Equipment to move flue gases and control air pollution typically require large amounts of auxiliary 

power to properly operate equipment. Even small upgrades to the equipment can decrease auxiliary 

power requirements while maintaining emission reduction levels. 

2.3.3.2.1 Economizer 

The economizer extracts energy from the combustion gases as they exit the furnace. This energy 

is used to preheat the water returning to the boiler. This heat exchanger improves EGU efficiency 

by reducing the amount of fuel required to convert water into steam. The replacement or 

refurbishment of the economizer can lead to gross heat rate improvements of around 50-100 

Btu/kWh. 

2.3.3.2.2 Sootblowers 

Coal combustion produces ash that builds up on tubing surfaces, reducing heat transfer for steam 

generation and resulting in higher gross heat rates. Sootblowers remove built-up coal ash to restore 

performance of the heat transfer surfaces. Intelligent sootblowers monitor system conditions and 

activate when boiler operation indicates the need to remove ash rather than activating at arbitrary 

time intervals set by operators. These systems use real-time furnace data to identify specific areas 

requiring ash removal. They can improve gross heat rates 30-150 Btu/kWh. 

2.3.3.2.3 Air heaters 

Air heaters transfer heat between the incoming pre-combustion air and the exiting flue gas. By 

preheating the combustion air, less fuel is required to convert pre-heated water to steam. Due to 

their design, leakage can occur and, as a result, reduce the pre-combustion air temperature, 

requiring greater coal consumption. Leakage also increases auxiliary power requirements since 
2 – 15 

equipment must now handle the increased volume of air from the in-leakage. Properly maintained 

air heaters limit leakage to below 6 percent. Reducing leakage can reduce the auxiliary power 

requirements and, therefore, the net heat rate by 10-40 Btu/kWh. 

Another method to improve heat rate is to extract more energy across the air heater to raise precombustion air temperature. Typically, the air heater outlet is maintained at 20-30°F above the 

sulfuric acid dew point to prevent corrosion. Depending on the air heater’s size, major 

modifications may be required to capture more energy, but the modifications might reduce gross 

heat rate by 50-120 Btu/kWh. 

2.3.3.2.4 Induced draft fans and forced draft fans 

Induced draft fans and forced draft fans typically operate at constant speed with dampers to control 

flue gas flow rate. Using dampers is the conventional choice to control flow while the fan motor 

runs at constant speed. However, newer methods are available to control flow for more efficient 

operation while reducing auxiliary load (e.g., variable frequency drives, variable pitch blades).

These upgrades or replacements provide net heat rate improvements of 10-50 Btu/kWh. 

2.3.3.2.5 Flue gas desulfurization 

A variety of flue gas desulfurization systems are used at existing coal-fired EGUs. Some EGUs 

still employ outdated venturi-type designs that create large pressure drops and require greater 

auxiliary power to operate the induced draft fans. Upgraded designs, such as co-current spray tower 

quenchers, can replace the outdated designs and enhance net heat rate. 

A more common flue gas desulfurization technology installed throughout the fleet is a spray tower 

absorber, also known as a wet scrubber. Technology upgrades, including installing turning vanes 

to reduce pressure drop and improve gas flow, can reduces induced fan auxiliary power 

requirements and enhance net heat rate. 

2.3.3.2.6 Electrostatic precipitator 

Efficient electrostatic precipitator operation involves maintaining the maximum applied voltage, 

without arcing. Arcing represents lost energy unusable for particulate removal and requires the 

field be re-energized, consuming additional auxiliary power. Electrostatic precipitator upgrades 

can improve performance by eliminating arcing and reducing auxiliary power consumption. 

2.3.3.2.7 Selective catalytic reduction 

Innovations in selective catalytic reduction systems have reduced auxiliary energy requirements, 

such as the use of hot secondary air or auxiliary steam for heating system equipment (instead of 

electrically heating ambient air). Catalyst design can lower pressure drop across the reactor and 

reduce induced fan auxiliary power requirements. 
2 – 16 

2.4 External studies of heat rate improvement potential at existing coal-fired EGUs 

There are a number of technical and statistical studies that explore opportunities to improve heat 

rate at existing coal-fired EGUs in the U.S. In the proposal TSD, EPA summarized five studies 

that found potential heat rate improvements of between 8.7 and 15 percent from existing coal-fired 

EGUs. Commenters provided information on additional studies, including government and 

independent research reports that assess potential heat rate improvements from a large study 

population of coal-fired EGUs. In general, the concepts for improving heat rate or reducing CO2

emissions presented in these studies focus on: reducing heat losses, extracting more energy from 

the steam cycle, replacing worn or degraded equipment, installing equipment upgrades, increasing 

data measurement and tracking to enhance operation and efficiency, conducting routine 

maintenance to sustain peak performance, hiring staff dedicated to heat rate improvement and 

management, and instituting a corporate culture that places emphasis on efficiency improvements. 

The studies use a variety of approaches to estimate potential heat rate improvement and CO2

emission reductions. For example, some studies used engineering assessments, some 

subcategorized EGUs by specific design characteristics or operating conditions and then compared 

EGUs in each subcategory against one another, and others compared each EGU’s heat rate 

performance or CO2 emission rate over time. Summaries of several of the studies referenced in the 

proposal TSD are presented below. 

NETL produced a number of reports and workshop summaries about improving efficiency at 

existing coal-fired EGUs. In a 2009 workshop to “explore opportunities to improve the thermal 

efficiency of existing and future coal-fired power plants,” NETL brought together 18 leading 

industry experts representing EGU operators, equipment vendors, energy consultants, and industry 

associations to analyze the technical and non-technical issues affecting power plant efficiency.11

The analysis identified a wide variation in efficiency levels across the EGU fleet, but found 

numerous opportunities to improve efficiency exist, such as: (1) decreasing excess oxygen to the 

boiler, (2) installing variable speed motors, (3) utilizing waste heat for coal drying, and (4) 

employing solar systems to heat feed water. The report identifies several barriers that limit the 

adoption of measures to improve heat rate, including: (1) lack of economic incentives to improve 

efficiency because of fuel adjustment clauses that enable electricity generating companies to “pass 

through” changes in fuel costs directly to customers, (2) lack of management commitment for 

efficiency programs, (3) focus on optimizing profitability and ensuring EGU availability over 

efficiency, and (4) lack of on-site performance engineers dedicated to heat rate improvement. In 

general, the report found that while heat rate improvements and efficiency are important, potential 

 

11 See J Eisenhauer and R Scheer, 2009. Opportunities to Improve the Efficiency of Existing Coal-fired Power Plants. 

NETL Workshop Report. 2009 July 15-16 in Rosemont, IL. Available at 

http://www.netl.doe.gov/File%20Library/Research/Energy%20Analysis/Publications/OpportImproveEfficExistCFP

P-ReportFinal.pdf. 
2 – 17 

fuel cost savings represent only a small portion of total costs and are “of secondary concern when 

compared to needs for maintaining system reliability.” 

A second NETL workshop was conducted with 53 leading industry experts to explore technical 

opportunities to improve efficiency, identify barriers, and identify initiatives to enhance efficiency

across the fleet.12 The workshop participants developed a list of more than 50 distinct opportunities 

to improve efficiency, many of which are broadly applicable “across most of the fleet of coal-fired 

[EGUs],” including better sensing, measurement, and control; better and more frequent 

maintenance; improved fuel handling; and capital upgrades and improvements. 

In 2010, NETL published a report that estimated existing coal-fired EGUs could enhance 

efficiency from a fleet wide generation-weighted efficiency of 32.5 percent in 2008 to 35.2 percent, 

equivalent to a heat rate improvement of about 9 percent.13 To develop the estimate, NETL 

segmented coal-fired EGUs into 13 groups based on steam cycle, coal type, and capacity. Using 

data from 1998 to 2008, the researchers identified the 90th percentile EGU,14 ranked by efficiency, 

within each of the 13 segments and calculated the potential efficiency improvements if all EGUs 

below the 90th percentile improved their average efficiency to the level of the 90th percentile EGU 

in the segment. Aside from the impact of steam cycle, coal type, and capacity, the NETL study 

found that other design characteristics, location, and emission air pollution controls did not account

for variations in plant efficiency. NETL also conducted an alternate analysis that evaluated existing 

coal-fired EGUs’ past performance between 1998 and 2008 and found that if each EGU returned 

to its own best historical heat rate, overall fleet wide heat rate would improve more than 6 percent. 

Lehigh University’s Energy Research Center (ERC) compiled techniques for improving heat rate, 

and a 2009 update stated improvements in net heat rate of up to 15 percent for existing coal-fired 

EGUs burning Powder River Basin subbituminous coal and lignite coal, and 10 percent for 

bituminous coal were possible if improvements could be made “in all possible areas.”15 However, 

the authors note that it would not be possible to take full advantage of all opportunities at all EGUs, 

but the information demonstrates that there are substantial opportunities to improve heat rate. The 

 

12 See R Brindle, J Eisenhauer, A Greene, M Justiniano, L Kishter, M Munderville, R Scheer, 2010. Improving the 

Thermal Efficiency of Coal-Fired Power Plants in the United States. Workshop Report. 2010 February 24-25 in 

Baltimore, MD. Available at 

http://www.netl.doe.gov/File%20Library/Research/Energy%20Analysis/Publications/ThermalEfficCoalFiredPower

Plants-TechWorkshopRpt.pdf. 

13 See P DiPietro and K Krulla, 2010. Improving the Efficiency of Coal-Fired Power Plants for Near Term Greenhouse 

Gas Emissions Reductions. NETL Report DOE/NETL-2010/1411. Available at 

http://www.netl.doe.gov/File%20Library/Research/Energy%20Analysis/Publications/DOE-NETL-2010-1411-

ImpEfficCFPPGHGRdctns-0410.pdf. 

14 NETL ranked heat rate in the opposite order of many of the other studies discussed below. Therefore, the 90th

percentile in the NETL study is methodologically equivalent to the 10th percentile performer in the studies discussed 

below. 

15 See Lehigh University Energy Research Center, 2009. “Reducing Heat Rates of Coal-fired Power Plants,” Lehigh 

Energy Update, 27(1). Available at http://www.lehigh.edu/~inenr/leu/leu_61.pdf. 
2 – 18 

research includes improvement opportunities from process optimization, more aggressive 

maintenance practice, and equipment design modifications. 

The Congressional Research Service (CRS) published a report summarizing NETL and other 

analyses. 16 The report notes that as coal-fired EGUs age, the EGUs lose efficiency due to 

mechanical wear; minimizing energy losses presents the greatest opportunity for regaining 

efficiency through equipment refurbishment, improved operations, and dedicated maintenance 

programs. For example, the top 10 percent of EGU’s operate on average 3.5 percentage points 

more efficiently than the remaining fleet. Consequently, there exists room for improvement. 

Lashof et al. of the Natural Resources Defense Council (NRDC) analyzed the potential heat rate 

improvements from coal-fired EGUs from a suite of heat rate improvement measures at EGUs 

with the highest heat rates for their thermodynamic steam cycle category.17 The study ranked coalfired EGUs by their full-load gross heat rate. EGUs that ranked higher than the 10th percentile for 

their category, but lower than the median (i.e., 50th percentile) were assumed to have potential heat 

rate improvements of 300 Btu/kWh and EGUs ranked greater than the median for their category 

were assumed to have potential heat rate improvements of 600 Btu/kWh. The 600 Btu/kWh heat 

rate improvement is based on measures outlined in the NRDC report (e.g., combustion 

optimization software and controls, upgrading economizers, cleaning and tuning steam 

condensers). 

Linn et al. examined annual heat rate data from approximately 1,000 coal-fired EGUs between 

1985 and 2009, grouping plants by boiler type, age, size, and other characteristics.18 The RFF study 

estimates that if coal-fired EGUs in each category improved heat rate to the level of the EGU with 

the 10th percentile heat rate in the category, overall gross heat rate would improve by 5.5 percent. 

Sargent & Lundy, LLC (S&L) developed an engineering study for EPA of potentially applicable 

heat rate improvement methods for coal-steam EGUs. The S&L study, cited in the proposal TSD, 

presents technical descriptions of numerous well proven, commercially available heat rate 

improvement techniques. The study also includes indicative ranges of potential heat rate 

improvement (Btu/net kWh) with associated indicative ranges of capital and O&M costs at three 

unit sizes (200, 500, and 900 MW). 

 

16 See RJ Campbell, 2013. Increasing the Efficiency of Existing Coal-Fired Power Plants. CRS Report R43343. 

Available at http://fas.org/sgp/crs/misc/R43343.pdf. 

17 See DA Lashof, S Yeh, D Doniger, S Carter, L Johnson, 2013. Closing the Power Plant Carbon Pollution Loophole: 

Smart Ways the Clean Air Act Can Clean Up America’s Biggest Climate Polluters. Available at: 

http://www.nrdc.org/air/pollution-standards/files/pollution-standards-report.pdf. 

18 See J Linn, E Mastrangelo, and D Burtraw, 2014. “Regulating Greenhouse Gases from Coal Power Plants under the 

Clean Air Act,” Journal of the Association of Environmental and Resource Economists. 1(1/2): 97-134. (DOI: 

10.1086/676038). First published as 2013 working paper RFF-DP-13-05 available at 

http://www.rff.org/rff/Documents/RFF-DP-13-05.pdf. 
2 – 19 

Several leading energy sector engineering consultants produced conference papers and 

presentations that summarize real-world projects and/or the consultants’ experience with heat rate 

improvement projects. In a conference paper, Storm indicated that there were at least 22 O&M 

heat rate factors that are controllable by the EGU operator, including (1) reducing air in-leakage 

into the boiler; (2) improving fineness of fuel by optimizing coal pulverizers; (3) reducing air 

heater leakage; (4) optimizing primary, overfire air, and secondary air flow measurement and 

control; and (5) balancing fuel and air distribution into the burner belt. The consultant estimates 

that the average large coal-fired EGU has about 300 to 500 Btu/kWh potential heat rate 

improvement (i.e., about 3 to 5 percent for an EGU with a heat rate of 10,000 Btu/kWh). The 

consultant goes on to say that “exacerbating the challenge [to improve heat rate] is the fact that 

often major plant overhauls have been changed from 6 months between boiler scheduled outages 

to sometimes more than 2 years.”19

 

Commenters submitted independent studies to inform EPA’s analysis of potential heat rate 

improvement, including engineering analyses, statistical analyses, and case studies. The results of 

the commenters’ studies provide similar results to EPA’s potential heat rate improvement analyses 

described in the following section. The commenters’ studies are summarized below. 

Sierra Club and Earthjustice submitted an independent study comparing each coal-fired EGU’s 

best rolling 365-day gross heat rate between 2001 and 2012 against the EGU's own average gross 

heat rate.20 The study included 52 randomly selected coal-fired EGUs designed to represent the 

coal-fired EGU fleet as a whole (based on 2012 electricity generation). The study uses rolling 365-

day averages of daily gross heat rates in order to minimize the influence of weather and electricity 

generation, and to represent long-term trends. For each EGU, the EGU’s lowest and 95th percentile 

rolling 365-day average gross heat rate were compared to the EGU’s 12-year average gross heat 

rate (2001-2012). The results of the analysis indicate that, on average, the coal-fired EGUs in the 

study achieved and maintained gross heat rates over a 365-day period that were 7.6 or 6.1 percent 

lower at the lowest and 95th percentile gross heat rate, respectively. 

The General Electric Company (GE) submitted an assessment of technical, economic, and market 

potential for heat rate improvements at coal-fired EGUs.21 The assessment identified equipment 

upgrades and best practices that, individually, could improve heat rate between 0 and 2.9 percent. 

The study estimated the average technical potential for gross heat rate improvements to be 5.8 for 

large coal-fired EGUs to 6.8 percent for smaller coal-fired EGUs. To calculate the economic 

 

19 See D Storm, 2009. Applying the Fundamentals for Best Heat Rate Performance of Pulverized Coal Fueled Boilers. 

EPRI 2009 Heat Rate Conference Paper. 2009 February 3-5 in Albuquerque, NM. Available at 

http://www.stormeng.com/pdf/EPRI2009HeatRateConference%20FINAL.pdf. 

20 See Comments of Sierra Club and Earthjustice. Available at 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2013-0602-24029. Also, see B Buckheit and N 

Spiegel, 2014. Sierra Club 52-Unit Study: Data Files. Available at 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2013-0602-25475. 

21 See Comments of The General Electric Company. Available at 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2013-0602-22971. 
2 – 20 

potential, GE estimated the penetration rate for equipment upgrades and best practices by 

establishing a “best in class” full load net heat rate benchmark (9,400 Btu/kWh), irrespective of 

EGU design characteristics, and compared each EGU’s full-load net heat rate to the “best in class” 

benchmark to determine potential heat rate improvements. The study then constrained equipment 

upgrades and best practices to those with costs below the social cost of carbon, resulting in 

estimated heat rate improvement of 6.3 percent for large coal-fired EGUs to 5.3 percent for small 

coal-fired EGUs. Finally, the study includes an assessment of market potential based on results 

from the North American Electricity & Environmental Model (NEEM). For the modeling study, 

each state was constrained to the CO2 emission rate from EPA’s proposal; coal-fired EGUs for 

which the study assumed some heat rate improvement measures were implemented were only 

offered the option to use the most expensive heat rate improvements (on the assumption that the 

lower price options were already implemented at the EGU.) Using the least-cost electricity utility 

planning framework of NEEM, the modeling study estimates that only 1 percent heat rate 

improvement will be implemented and the remaining CO2 reductions will come from other actions 

within each state. 

In addition to the reports described above, POWER Magazine published an article on heat rate 

improvement opportunities at coal-fired EGUs.22 Korellis summarized an Electric Power Research 

Institute (EPRI) report of heat rate improvement case studies at five large coal-fired EGUs with 

electricity generating capacities of 95 to 650 MW, combusting Powder River Basin subbituminous 

(3 EGUs) and bituminous (2 EGUs) coal, and utilizing different thermodynamic steam cycles (2 

supercritical and 3 subcritical). Through a combination of best practices and equipment upgrades, 

four of the five EGUs were able to improve net heat rate by 3 to 5 percent (see Table 2-1). Common 

issues at the EGUs prior to the heat rate improvement projects included: (1) problems related to 

combustion and high air heater/stack gas temperatures, (2) shortage of heat rate data, (3) lack of 

training and understanding of controllable losses, (4) lack of performance testing, (5) problems 

with feed water heater train performance, and (6) buildup of soot on heat transfer surfaces. The 

article provides a list of general and EGU-specific heat rate improvement recommendations with 

estimated reductions in heat input. The article also provides a summary of a utility’s “fleet wide” 

heat rate improvement assessment. The engineering study indicated that the 12 coal-fired EGUs 

in a member utility’s fleet could improve net heat rate by approximately 5.3 percent at a cost of 

approximately $40 per ton of CO2 reduced. The summary of the full report states, “[r]educing a 

power plant's heat rate can lower emissions, fuel consumption, and costs, thus contributing to the 

plant's bottom line. A significant improvement in heat rate can often be achieved with a re-

 

22 See S Korellis, 2014. “Coal-Fired Power Plant Heat Rate Improvement Options, Part 2,” Power Magazine. Available 

at http://www.powermag.com/coal-fired-power-plant-heat-rate-improvement-options-part-2. For the full report, see 

EPRI, 2014. Range and Applicability of Heat Rate Improvements. Technical Update 3002003457. Available at 

http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=000000003002003457. This supplementary 

study expands upon and confirms the large quantities of similar information presented in this TSD. 
2 – 21 

commitment to best operating practices, which can minimize the need for capital expenditures on 

new technology.” 

Table 2-1 EPRI net heat rate improvement case study at five coal-fired EGUs 

EGU Project type Net heat rate 

improvement 

A Implementing multi-level heat rate teams, training, managing 

excess air 

279 Btu/kWh 

B Aligning cycles, reducing air inleakage, chemically cleaning 

turbine 

557 Btu/kWh 

C Maintaining steam path and replacing feed water heater 400 Btu/kWh 

D Reducing air inleakage, replacing feed water heater, cleaning 

condenser and feed water tubes 

500 Btu/kWh 

E [Not available] Heat rate increased 

(350 Btu/kWh) 

A report by the Energy Information Agency (EIA) used data from 1,027 coal-fired EGUs to 

estimate potential net heat rate improvements.23 The coal-fired EGUs were grouped into quartiles 

based on the difference between reported and predicted net heat rates. Those EGUs with the least 

difference between the reported and predicted net heat rates (i.e., 1st quartile) were assumed to 

have the least potential for net heat rate improvement while those with the largest difference (i.e.,

4

th quartile) were assumed to have the most potential for net heat rate improvement. Using a matrix 

of 29 heat rate improvement measures (out of 56 identified in the study), the study determined that 

net heat rate for the 1,027 coal-fired EGUs could be improved, on average, by approximately 2.0 

to 5.5 percent at capital costs of approximately $300/kW of capacity. 

These studies provide technical information about potential heat rate improvement at existing coalfired EGUs, including available best practices and equipment upgrades. As several commenters 

noted, these studies demonstrate that heat rate improvements are both readily available and 

achievable. The studies and commenters’ suggestions informed EPA about the different 

 

23 See EIA, 2015. Analysis of Heat Rate Improvement Potential at Coal-Fired Power Plants. Available at 

http://www.eia.gov/analysis/studies/powerplants/heatrate/pdf/heatrate.pdf. This supplementary study expands upon 

and confirms the large quantities of similar information presented in this TSD. 
2 – 22 

approaches and analytical techniques that are available to assess potential heat rate improvements 

at existing coal-fired EGUs. The agency used the information to enhance the analytical approaches, 

but decided not to adopt any one of the studies described above. The agency believes its analyses, 

described in the next section, are more appropriate for the purpose of determining potential heat 

rate improvement for this final rule because the agency’s approach: (1) uses more recent data on 

nearly all existing coal-fired EGUs in the CONUS; (2) compares EGUs’ 2012 performance against 

the EGUs’ best performance, largely obviating the need for subcategorization or a “one-size-fitsall” heat rate or CO2 emission rate; and (3) controls for key ambient and operating conditions 

outside the EGU operators’ control. 

2.5 EPA study of heat rate improvement potential at existing coal-fired EGUs 

To assess potential heat rate improvements at coal-fired EGUs, EPA began by looking at each 

coal-fired EGU’s past gross heat rate performance. EPA used three analytical approaches to assess 

potential heat rate improvement for each interconnection by comparing the EGUs’ best historical 

gross heat rates between 2002 and 2012 against the EGUs’ own 2012 gross heat rate. The agency 

based this approach on the reasonable expectation that, in the general sense, if coal-fired EGUs in 

an interconnection were able to demonstrate and achieve specific heat rates in the past, the EGUs 

should be able to achieve similar heat rates again. The advantages of such an approach are (1) it 

compares each EGU against the EGU’s own past performance, thereby controlling for many of the 

design characteristics that vary among EGUs but are constant or nearly constant over time at 

individual EGUs (e.g., generator capacity, thermodynamic steam cycle, coal type, cooling system 

type); (2) it accounts for heat rate improvement measures employed during the study period by 

using 2012 as the baseline, (3) it is not significantly affected by the addition of pollution control 

devices during the study period because the auxiliary power consumption from these devices has 

negligible impact on gross electricity generation;24 and (4) the historical unit-level gross heat rate 

is by definition demonstrated and achievable by the respective coal-fired EGU. 

In the June 2014 proposal, EPA proposed a 6 percent potential heat rate improvement for the coalfired EGU fleet for the purpose of establishing state emission goals. The potential heat rate 

improvement was based on two components: (1) a statistical analysis that determined 4 percent 

 

24 In addition to the parasitic load from add-on pollution controls, another effect of some add-on controls is worth 

mentioning here. The installation of wet flue gas desulfurization (i.e., wet FGD or scrubber) technologies can affect 

heat rate because the chemical process of capturing SO2 creates CO2 that is measured in the continuous emission 

monitoring systems. This results in higher calculated heat input (see infra note 30). However, the typical increase in 

heat rate is less than 1 percent and may be offset by heat rate improvements resulting from switching to lower-moisture, 

higher-sulfur bituminous coal. Furthermore, the impact on calculated heat input is only relevant to EGUs that added 

WFGD during the study period because the analytical approaches described below evaluate the change in heat rates. 

Assuming consistent operation and fuel sulfur content, an EGU with a WFGD during the entire study period would 

not have a heat rate change due to the WFGD CO2. Circulating fluidized bed (CFB) boilers also have higher CO2

emissions from the chemical process of limestone injection to capture SO2, thereby affecting the heat input calculations. 

However, because CFBs generally injected limestone at all times throughout the study period, the EGUs would not 

have heat rate changes due to limestone injection. See section 2.6.3 for more information about the impact of CO2

emissions from SO2 pollution control devices. 
2 – 23 

improvement in the fleet wide coal-fired EGU heat rate is possible through best practices (e.g.,

improved operation and maintenance), and (2) an estimate that a 2 percent heat rate improvement 

is possible through additional equipment upgrades. 

Several commenters suggested these values were too conservative, suggesting the agency should 

require greater heat rate improvements from the fleet on average or from each and every coal-fired 

EGU. Some of these commenters provided studies and analyses showing larger heat rate 

improvements are feasible for coal-fired EGUs (see summaries in section 2.4). EPA carefully 

reviewed the comments and supplemental materials before refining the potential heat rate 

improvement analysis. As explained below, the analysis for this final rule does not include a 

separate estimate of the potential from equipment upgrades. 

Conversely, several commenters suggested EPA’s proposed heat rate improvement was too 

optimistic, providing engineering studies and data describing heat rate improvement measures that 

had already been implemented at specific coal-fired EGUs. Commenters also expressed concern 

that EPA improperly characterized the potential heat rate improvements from equipment upgrades 

because: (1) the Sargent & Lundy (S&L) report cited in the proposal TSD is based on “conceptual” 

equipment upgrades, (2) plant operators indicate they have already implemented many of the heat 

rate improvement measures identified in the S&L report, (3) heat rate improvements from the list 

of equipment upgrades and best practices in the S&L report may not be additive (i.e., cumulative), 

and (4) the costs of equipment upgrades are highly site specific. The EPA generally agrees with 

the four points noted above, and alluded to these considerations in the proposal TSD, section 2.6.1 

Heat Rate Improvement Capital Cost Assumptions. The points are also discussed in more detail 

below. 

The agency does not agree that the EPA improperly characterized or misused information from 

the S&L study.25 The S&L study was prepared for EPA, and agency senior engineering staff with 

previous experience in fossil power plant engineering, operations, and management were involved 

in the scoping and review of the study report. The agency recognized the limits of the study and 

considered the S&L caveats/limitations when using information from the study, including the 

following statements from the S&L study: 

1. The primary intent of the study was to focus on methods that have been successfully 

implemented by the utility industry. 

2. This study identifies specific plant systems and equipment where efficiency 

improvements can be realized either through new installations or modifications, and 

provides estimates of the resulting net plant heat rate reductions and the order-ofmagnitude costs for implementation. To conduct the study presented in this report, S&L 

 

25 See Sargent & Lundy, 2009. Coal-fired Power Plant Heat Rate Reductions. Available at 

http://www.epa.gov/airmarkets/documents/ipm/coalfired.pdf. 
2 – 24 

surveyed available literature, spoke with technology manufacturers, and used its 

engineering expertise as the basis. 

3. This report discusses potential efficiency improvement concepts and the resulting heat 

rate reductions that can be implemented in various systems of a typical coal-fired power 

plant. All estimated capital and installation costs are referenced from work in progress and 

vendor quotes as of the year 2008. 

4. S&L cautions that the costs presented herein are not indicative of those that may be 

expected for a specific facility due to variables such as equipment, material, and labor 

market conditions and site specifications. However, these cost estimates provide valuable 

information for comparative purposes when evaluating the advantages and disadvantages 

of the various concepts. The costs should not be used as a basis for project budgeting or 

financing purposes. 

In consideration of these caveats/limitations, the agency used the average conceptual performance 

values from the S&L study to develop an estimated 4 percent potential heat rate improvement that 

the average coal-fired EGU could make from only a few of the higher-cost equipment upgrades. 

The agency then discounted that potential by 50 percent to obtain the 2 percent potential heat rate 

improvement estimate attributable to equipment upgrades that was included in the proposal. The 

50 percent discount was applied to account for site-specific factors that could limit the potential 

heat rate improvement, including the fact that the cumulative effect of installing multiple measures 

can be less than the sum of their individual effects. The agency similarly used averages of S&L 

order-of-magnitude costs for thirteen of the fifteen S&L heat rate improvement measures26 to 

develop an indicative conceptual cost estimate ($100/kW) for an overall 6 percent heat rate 

improvement. That estimate was conservative because 4 percent was projected to come from 

relatively inexpensive best practices while only 2 percent was projected to come from a small 

number of more expensive “new installations or modifications” described in the S&L study. The 

average $100/kW cost, derived from S&L estimates for unit sizes ranging from 200 to 900 MW 

inherently reflects the inverse relationship of $/kW cost to MW unit size, and was thus 

appropriately applied in the agency’s estimate of the fleet-wide average cost of heat rate 

improvement. The agency was also mindful of the 2008 time frame of the S&L cost estimates. 

EPA considered the 2008 cost values to be applicable as 2011 values because, through a 

coincidence of timing, the 2008 study reflected the highest cost levels experienced by the power 

sector up to that point in time. The economic recession that commenced in late 2008 caused costs 

to decrease sharply before beginning a slow recovery. The agency therefore considered the S&L 

cost values to quite reasonably represent the costs in 2011.27

 

26 Three mutually exclusive fan and fan drive modifications were discussed in the S&L study. Accordingly, EPA 

conservatively selected the fan-related measure that included both a fan upgrade and variable frequency drive for use 

in the cost estimate because the selected measure had the highest estimated capital cost. 

27 Cost estimates for implementation of the final rule are based on 2011 dollars. 
2 – 25 

In addition to the S&L report, EPA referred to several other studies in the proposal TSD that 

present similar information on equipment upgrades to the S&L report. As demonstrated by the 

materials provided by commenters expressing a preference for larger heat rate improvements, there 

are further analyses that show a greater than 6 percent heat rate improvement is achievable at coalfired EGUs. One commenter also noted that many existing smaller coal-fired EGUs have recently 

retired or will be retiring over the next decade (see section 2.6.5 below for more information about 

retirements), leaving more of the larger, well-controlled coal-fired EGUs that are similar to the 

example EGUs used in the S&L report. Furthermore, the equipment upgrades and best practices 

identified in the proposal TSD and supporting reports are only intended to serve as illustrative lists

of measures that coal-fired EGUs might employ to improve heat rate. 

In the refined heat rate analyses for this final rule, the results are not based on heat rate 

improvements that would be achieved by implementation of specific measures. Instead, the heat 

rate analyses are based on each coal-fired EGU’s best historical heat rate performance as well as 

its 2012 heat rate performance. It reflects the heat rate improvements that would occur if EGUs 

performed at heat rates that they have already demonstrated are achievable. As one commenter 

phrased it, EPA is simply basing building block 1 on a continuation of the types of good 

maintenance and operating practices that are necessary for EGUs overall to maintain the better 

heat rates they have previously achieved. 

Some commenters noted that if a coal-fired EGU implemented equipment upgrades during the 

study period (2002–2012), improvements to gross heat rate would show up in the historical gross 

heat rate data and could result in double counting because the heat rate improvements would affect 

the statistical results and would still be expected from the equipment upgrade component. 

Similarly, many commenters provided lists of equipment upgrades undertaken at specific coalfired EGUs, arguing that the equipment upgrade component of the agency’s potential heat rate 

improvements does not account for upgrades that coal-fired EGUs have already implemented. EPA 

acknowledges that there could be overlap between the two components of the potential heat rate 

results, but notes that the agency was not requiring individual coal-fired EGUs to achieve specific 

heat rate improvements. However, for this analysis the agency no longer uses the two components 

to identify potential heat rate improvements. Instead, the agency conducts the statistical analyses 

described below to compare each EGU’s best historical performance to its 2012 performance. The 

results reflect the overall heat rate improvement for coal-fired EGUs in each regional 

interconnection if those EGUs had returned to their demonstrated best historical gross heat rates 

from the baseline year of 2012. This backward-looking approach is inherently conservative 

because it does not reflect opportunities for heat rate improvements from additional best practices 

and equipment upgrades that could have been (and still can be) cost-effectively implemented at 

coal-fired EGUs. 

In the proposal TSD, EPA’s statistical analysis compared each EGU’s best historical gross heat 

rate to the EGU’s 11-year average gross heat rate. Commenters stated that some coal-fired EGUs 
2 – 26 

made significant investments to improve heat rate during the 11-year study period and the use of 

an 11-year baseline fails to recognize those investments. These commenters suggested the agency 

could adjust the potential heat rate improvements to reflect any upgrades already implemented at 

coal-fired EGUs. In the revised analyses described below, in order to account for actions 

undertaken to improve gross heat rates at coal-fired EGUs, the agency refined the methodology to 

compare each EGU’s best heat rate performance to its 2012 gross heat rate. Therefore, any actions 

an EGU implemented to improve gross heat rate during or before 2012 (and maintained in 2012) 

would be accounted for in the analysis and would result in a reduction in the estimated potential 

heat rate improvement. In addition, the selection of 2012 as a baseline is conservative because 

many commenters noted that coal-fired EGUs experienced greater cycling and lower capacity 

factors in the later years of the study period (2002-2012). 

Several commenters suggested that EPA should replace the statistical analysis with unit-specific 

engineering studies to establish unit-level heat rate improvement goals. These commenters 

contended that such studies are the only method for accurately identifying what types of equipment 

upgrades and best practices (and therefore, potential heat rate improvements) are feasible and cost 

effective at each coal-fired EGU. As discussed in sections V.A and V.C of the preamble and the 

response to comments, conducting engineering studies to determine the potential for improving 

heat rate through best practices and equipment upgrades at every coal-fired EGU would be 

unnecessarily lengthy and expensive, and would not be consistent with the broader goal-setting 

purpose of this rulemaking. However, the agency does believe that EGU owners and operators that 

conduct such studies will discover additional cost-effective opportunities to improve heat rate at 

their EGUs. Commenters also suggested that as an alternative to unit-level engineering studies, 

EPA could identify unit-level heat rate improvement goals. EPA’s methodology does assess the 

potential heat rate improvement for each interconnection by comparing an individual EGU’s best 

demonstrated historical gross heat rate relative to the EGU’s own 2012 gross heat rate. However, 

as several commenters note, and the agency explained in the proposal, this rule does not establish 

mandatory federal heat rate performance requirements for coal-fired EGUs or mandate heat rate 

improvement as a compliance strategy. The agency uses the results of the heat rate improvement 

analysis for each regional interconnection (i.e., the electricity system) along with emission 

reductions from the other building blocks of the best system of emission reductions to establish 

state-level goals. Each state has the flexibility to identify measures in its state plan to achieve the 

state goals and, if the state chooses, it may develop unit-specific heat rate improvement targets – 

or entirely ignore potential heat rate improvement. Commenters also stated that the proposal 

expressly anticipates fleet wide averaging of CO2 emission intensity improvements, thereby 

offering greater opportunities to achieve the state goals at lower cost relative to controlling 

emissions or heat rate at the plant or unit level. Therefore, the agency believes it is appropriate in

this rulemaking to assess potential heat rate improvement at a scope broader than the unit-level. 

Commenters also recommended that EPA subcategorize coal-fired EGUs and develop specific 

heat rate improvement goals for each subcategory to account for the influence of different design 
2 – 27 

characteristics and fuel choices on an EGU’s heat rate. However, as discussed above and noted in 

the proposal TSD, the agency is not establishing mandatory heat rate improvements that must be 

achieved at each and every coal-fired EGU. Instead, the agency is attempting to determine a 

reasonable heat rate improvement that, on average, the coal-fired electricity generating fleet can 

achieve, and to use the potential heat rate improvement to calculate state goals. In addition, the 

agency does not believe there is a need to subcategorize by design characteristics because the heat 

rate improvement potential is based on analyses of each EGU’s past performance. Because heat 

rate improvement is assessed using each EGU’s best historical and 2012 performance, the full 

analyses inherently account for design characteristics that do not change from year to year. 

Commenters also highlighted the role of age and coal rank on an EGU’s heat rate. The average 

age of coal-fired EGUs in the study population was approximately 42 years in 2012. To assess the 

impact of age, EPA grouped the 884 coal-fired EGUs by 10-year age increments (as of December 

2012). While there is a difference in median and average annual gross heat rates between coalfired EGUs less than 10 years of age and those greater than 10 years, there was no statistical 

difference in gross heat rates for the different groupings of coal-fired EGUs older than 10 years of 

age (i.e., the range of performance of EGUs 10-19 years of age is approximately the same as EGUs 

30-39 years of age). This indicates that operation and maintenance practices to manage degradation 

plays an important part in ensuring the efficiency of coal-fired EGUs. In addition, it is likely that 

the gross heat rate differences for younger units is due to other factors (e.g., advanced technology, 

larger boiler and/or steam turbine size) that do not change as the EGU ages. Therefore, the 

influence of age is unlikely to play an important role for an individual coal-fired EGU. 

An EGU’s primary coal rank may change, but changing coal ranks at a coal-fired EGU requires 

significant effort. Generally, coal-fired EGUs – especially pulverized coal boilers and their 

corresponding fuel handling equipment, combustion systems, and pollution control devices – are 

designed for the characteristics of a specific type of coal. Significant deviations from those coal 

characteristics can affect the EGU’s performance and reliability. Changes to coal type might 

require plant modifications to maintain maximum output and efficiency (e.g., pulverizer capacity, 

fan capacity, heating surface). Commenters noted that changing coal rank is likely not practical at 

many EGUs because coal rank is determined by the design of the EGU and environmental control 

systems. Although it might be a significant engineering endeavor, some coal-fired EGUs have 

switched coal types or blended different coal types. Furthermore, some EGUs might have the 

option of changing coals in the future, potentially improving their heat rate. Commenters also 

discussed the impact that coal quality (e.g., higher heating value, moisture content, ash content) 

can have on an EGU’s heat rate. While EPA recognizes the impact that coal rank and quality can 

have on an EGU’s heat rate, the agency also recognizes that EGU operators and coal suppliers 

typically set requirements for coal quality in purchase contracts, providing coal-fired EGUs with 

some level of control over the quality of fuel they purchase. Therefore, EPA did not control for 

coal rank or coal quality in the analysis. 
2 – 28 

In summary, the agency feels the analytical approaches described below, which are founded on 

each coal-fired EGU’s historical performance and its 2012 performance, account for nearly all 

design characteristics because these characteristics do not vary for a particular EGU between years 

or are accounted for by the use of 2012 as the baseline. Other factors, (i.e., ambient temperature 

and hourly capacity factor) that are beyond the EGU operator’s control are explicitly accounted 

for in some of the agency’s analyses presented below. Finally, for all of these analyses, by using 

long-term averages of heat rate (i.e., at least a year) or grouping large numbers of hourly values 

and accounting for outliers, EPA implicitly accounts for some of the variation that might be beyond 

the EGU operator’s control. Accordingly, subcategorization among affected coal-fired EGUs is 

not appropriate under building block 1. 

EPA used three approaches to calculate the potential heat rate improvement: (1) best historical 

performance, (2) best historical performance under similar conditions, and (3) efficiency and 

consistency improvements under similar conditions. Each of these approaches is described in more 

detail below. 

2.5.1 Heat rate, capacity factor, and temperature data 

Under 40 CFR Part 75, most fossil fuel-fired EGUs report hourly emission and operation data to 

EPA’s Clean Air Markets Division (EPA/CAMD). In 2012, 884 coal-fired EGUs reported heat 

input and gross electricity generation data to EPA.28 These 884 EGUs account for approximately 

1.61 billion tons of CO2 emissions in 2012, 96 percent of the 1.67 billion tons of CO2 emitted by 

the national coal-fired EGU fleet in 2012.29 Therefore, EPA considers these 884 coal-fired EGUs

representative of the coal-fired EGU fleet as a whole. 

EPA’s dataset includes the 884 coal-fired EGUs that reported heat input and gross electricity 

generation in 2012, including those EGUs that are near retirement. Some commenters suggested 

that the agency’s inclusion of coal-fired EGUs near retirement inflates the results of the heat rate 

improvement analysis because the soon-to-retire EGUs may have more variability in gross heat 

rate and higher gross heat rates due to frequent cycling, deferred or canceled maintenance, and 

termination of good heat rate practices. While the agency recognizes the commenters’ concern that 

soon-to-retire EGUs that operate with poor maintenance and operating practices can increase the 

potential heat rate improvements identified by the heat rate improvement analysis, the agency does 

not believe this artificially inflates the potential heat rate improvements. Any coal-fired EGU that 

stops implementing good maintenance and operations practices to maintain heat rates will have a 

similar type of impact on the results regardless of whether it is scheduled to retire to not. Also, the 

 

28 This population does not include coal-fired EGUs that did not operate in 2012, some of which retired before 2012 

and others which may have resumed operation after 2012. See supra note 5 for a discussion about treatment of 

cogeneration EGUs. 

29 CO2 emissions for the national coal-fired EGU fleet are taken from Table 12.6 Carbon dioxide emissions from 

energy consumption: electric power sector. EIA, 2015. Monthly Energy Review. Dated 2015 May 22 available at 

http://www.eia.gov/totalenergy/data/monthly/#environment. 
2 – 29 

agency believes that the inclusion of coal-fired EGUs that are near retirement is conservative for 

two reasons: (1) for states with mass-equivalent goals based on 2012 baseline levels, after soonto-retire EGUs cease operation they cease emitting CO2 contributing to attainment of the state goal, 

and (2) for states with CO2 intensity goals based on 2012 baseline levels, the retirement of coalfired EGUs with high heat rates in 2012 will contribute to achievement of the state goals. 

The data used in the analysis include hourly gross heat rate values. 40 CFR Part 75 requires that 

most coal-fired EGUs continuously measure emissions of CO2, NOX, and SO2, and report those 

hourly emissions along with hourly heat input30 and gross electricity generation to EPA/CAMD at 

the end of each calendar quarter. The monitoring regulation requires regular quality 

assurance/quality control (QA/QC) of the monitoring systems, including daily calibrations and 

semi-annual or annual relative accuracy tests.31 When EGU operators submit the hourly emission 

and operation data to EPA/CAMD, a responsible company official must certify that the data are 

true, accurate, and complete. In addition, the data undergo thousands of automated quality 

assurance tests as well as statistical analyses and EPA staff audits. Therefore, the agency believes 

these high-quality data are the best available information for assessing coal-fired EGUs’ 

performance over time. 

The monitoring regulation includes provisions to accommodate time periods when emission data 

are not available due to monitoring system malfunctions or maintenance, technical challenges, or 

missed QA/QC tests. When data are not available or deemed invalid (e.g., when a QA/QC test was 

not performed as required), the EGU is required to use specified data substitution methods that are 

designed to be conservative (i.e., overestimate emissions). This conservative bias is intended to 

create an incentive for better emission measurement – the overestimate incurs an economic penalty 

because at the end of the compliance period an EGU must surrender allowances equal to total 

reported emissions. Because of this conservative bias and the impact it would have on the results 

of this study, the agency excluded substitute data reported by EGUs from the dataset. These 

substitute data represent approximately two percent of all reported operating hours for the 884 

coal-fired EGUs during the study period. In addition, the agency excluded partial hours of 

operation that occur during the first hour of startup and the last hour of shutdown, and hourly gross 

heat rate values that were greater than or less than 2.6 standard deviations from an EGU’s annual 

average gross heat rate.32 Both of these conditions tend to occur during very low gross electricity 

generation. The hourly values ±2.6 standard deviations from the EGU’s annual average gross heat 

 

30 Heat input is derived using CO2 or O2 concentration and calculations detailed in 40 CFR Part 75 Appendix F. As a 

result, heat rate (Btu/gross kWh) is proportional to CO2 rate (CO2/gross kWh). 

31 The quality assurance requirements for monitoring systems are described in 40 CFR Part 75 Appendix B.

32 For datasets with a normal distribution, 99 percent of all values will be within 2.6 standard deviations of the mean. 
2 – 30 

rate may be due to unusual operating conditions or misreported gross electricity generation.33

These outliers account for approximately 1.9 percent of hourly data records. 

The monitoring provisions of 40 CFR Part 75 require regular QA/QC testing of the monitoring 

systems to ensure they are reading accurately. Commenters hypothesized that discrepancies 

between the monitoring system and the reference tests could explain much of the variation in heat 

rates. EPA assessed the monitoring systems’ relative accuracy and the findings indicate that the 

relative accuracy does not significantly affect the results of the agency’s analyses. In general, coalfired EGU operators are required to perform relative accuracy test audits (RATA) once per year if 

the difference between the monitoring system and RATA results is less than or equal to 7.5 percent, 

and twice per year if the difference is greater than 7.5 percent but less than 10 percent.34 If the flow 

monitoring system readings are statistically-significantly lower than the RATA results, the EGU 

operator must apply a bias adjustment factor (BAF) to all subsequent flow measurements to reduce 

potential underreporting of flow. There is no BAF applied for CO2 measurements. To assess the 

relative accuracy of the monitoring systems during the study period, EPA reviewed the EGU-level 

RATA results for the study period and then calculated the average annual generation-weighted 

relative accuracy within each of the interconnection regions. As shown in Table 2-2, the average 

(i.e., mean) relative accuracy35 for both CO2 and flow monitoring systems are reasonable – the 

difference between the reference tests and measured values is approximately 1.5 to 3.0 percent – 

and varies little from year to year within each interconnection. 

Table 2-2 Generation-weighted relative accuracy of CO2 

and flow monitoring systems, by interconnection 

 Western Texas Eastern 

CO2 Flow CO2 Flow CO2 Flow 

2003 2.57 2.70 2.27 2.30 2.52 2.22 

2004 3.17 2.51 2.63 1.64 2.51 2.11 

2005 3.21 2.95 2.82 2.25 2.69 2.10 

 

33 40 CFR Part 75 requires operators to apportion heat input to all EGUs sharing a common stack. For this reason, 

EGUs that are not generating electricity during startup or shutdown periods may report a small amount of gross 

electricity generation (e.g., 1 MW) to avoid errors in the apportionment calculations. However, this can result in 

unusually large gross heat rates. 

34 More information about the quality assurance provisions, including RATAs, can be found in 40 CFR Part 75 

Appendix B. 

35 Relative accuracy is expressed as the absolute value of the difference between the reference method measurements 

and the EGU’s pollutant concentration or flow measurements. However, the averages at the interconnection level are 

overstated because the relative accuracy is expressed as an absolute value. As a simple example, if the difference 

between the measurements at one unit is -2 percent and at another unit the difference is 2 percent, the arithmetic 

average is 0 [(-2+2)/2] while the average of the absolute values is 2 [(|-2| + |2|)/2]. EPA’s calculation is based on the 

absolute value. 
2 – 31 

 Western Texas Eastern 

CO2 Flow CO2 Flow CO2 Flow 

2006 3.06 2.81 2.85 1.33 2.56 1.99 

2007 3.15 2.59 2.25 1.50 2.61 2.06 

2008 3.41 2.86 3.68 1.59 2.75 2.12 

2009 2.95 2.97 2.47 1.76 2.55 1.97 

2010 2.65 3.07 2.86 2.00 2.45 2.05 

2011 2.40 2.59 2.56 1.69 2.63 2.02 

2012 2.33 2.56 2.46 1.53 2.41 2.13 

Notes: All values are generation weighted; 

Due to data management and archive policies, 2002 data 

were not available at the time of this analysis 

While the average relative accuracy at the EGU-level varies from 1 to 3 percent, the uncertainty 

becomes significantly less when these differences are aggregated over many EGUs – the relevant 

scope of analysis for building block 1 as a whole. EPA evaluated the combined effect of CO2 and 

flow measurement differences between the RATA and measurement systems at 685 coal-fired 

EGUs between 2009 and 2012.36 The results show that the combined differences across the 685 

EGUs over four years amount to a fleet wide standard “error” – an estimate of the uncertainty 

expected in fleet-wide emissions – of 0.08 percent.37 Therefore the agency believes the impact of 

monitoring system relative accuracy does not significantly influence the results of the potential 

heat rate improvement analyses. 

EPA also used data from NOAA’s Integrated Surface Data (ISD) and the USGS’s 

WaterQualityWatch (WQW). The NOAA dataset provides hourly ambient air temperature for over 

20,000 weather stations worldwide.38 Since coal-fired EGU heat rates can be sensitive to ambient 

air temperature and barometric pressure, the agency collected meteorological data from stations 

that are reasonably close to each coal-fired EGU's location and elevation to account for the impact 

of ambient conditions. For each of the 884 coal-fired EGUs, the agency identified the nearest 

 

36 Coal-fired EGUs with complex stacks (i.e., common stacks or multiple stacks) were excluded from the analysis. 

Coal-fired EGUs that converted to or from complex stacks between 2009 and 2012 are included only for years during 

which the EGUs did not use complex stacks. 

37 The average combined difference between the RATA and monitoring system for flow and CO2 measurements, using 

the directional difference (e.g., positive and negative values), is 0.5 percent and the standard deviation is 3.4 percent. 

To calculate the standard error, EPA used the following equation: 		


√

 where n is 3 years × 685 EGUs. 

38 NOAA’s Integrated Surface Data are available at http://www.ncdc.noaa.gov/data-access/land-based-stationdata/land-based-datasets/integrated-surface-database-isd. 
2 – 32 

weather station39 that reported a minimum of 8,400 hourly observations in the calendar year (i.e.,

greater than 95 percent of the hours in a year). Because of the constraint that a station must report 

at least 8,400 measurements in a calendar year, most of the 884 EGUs were associated with 

meteorological data from the two closest stations over the study period (2002-2012). The average 

distance between the EGUs and the weather stations was 22 miles and the average difference in 

elevation was only 366 feet.40 For at least one year of the study period (2002-2012), nine power 

plants did not have weather stations that met the three constraints described above. For these plants,

the agency used its judgement and assigned nearby weather stations that were within 100 miles 

and 1,000 feet elevation, but had as few as 7,000 hourly observations. In response to commenters’ 

concerns that EPA’s analysis did not account for cooling water temperature, the agency added data 

from the USGS WaterQualityWatch (WQW).41 

After excluding coal-fired EGUs that did not report heat input and gross electricity generation in 

2012 and coal-fired cogeneration EGUs that reported steam generation in one or more years during 

the study period, and excluding the data described above, EPA developed a dataset of hourly heat 

input, gross electricity generation, gross heat rate,42 capacity factor,43 and ambient air temperature 

for the years 2002 through 2012 for all 884 coal-fired EGUs. This dataset, which was included in 

the proposal,44 contained more than 61,848,500 hourly records. 

Commenters also suggested that EPA should use fewer years of data in its analysis, noting that the 

power sector is very different today than it was in 2002. EPA recognizes that the power sector 

went through many changes between 2002 and 2012, and is likely to go through many more 

changes between 2012 and 2022 – the first year of the interim implementation period. Nevertheless, 

the agency believes it is appropriate and beneficial to use the full 11 years of data. First, this time 

period includes a variety of national and regional economic conditions, fuel prices, electricity 

resource mixes, weather conditions (e.g., precipitation), and demand levels. As a result, it is more 

likely that future economic and weather conditions will be represented by data periods between 

2002 and 2012. Second, commenters specifically expressed concern that many coal-fired retired 

during the study period and might bias the results. However, EGUs that retired prior to 2012 are 

not included in the dataset. Only coal-fired EGUs that reported both heat input and gross electricity 

generation in 2012 are included in the dataset. Third, the foundation of EPA’s methodology uses 

 

39 EPA established two constraints on the locational differences of the weather station and the EGU: (1) less than 100 

miles in distance, and (2) less than 1,000 feet in elevation. 

40 The NOAA dataset is the most complete and comprehensive ambient air temperature data available to EPA. The 

hourly ambient air temperature of the nearest weather station is used only for the purpose of grouping hourly heat rate 

into temperature bins by 10° F increments. Therefore, the agency believes conditions at the nearest weather station 

provide a reasonable approximation of temperature conditions at the coal-fired EGU. 

41 The USGS WQW dataset provides hourly surface water temperature from approximately 2,000 sites around the U.S. 

The data are available at http://waterwatch.usgs.gov/wqwatch. 

42 EPA calculated gross heat rate as heat input (Btu) / gross electricity output (kWh). 

43 EPA calculated capacity factor as gross electricity output (kWh) / hourly gross electricity generating capacity (kWh). 

44 See supra note 1. 
2 – 33 

each coal-fired EGU’s best gross heat rate performance between 2002 and 2012 and the EGU’s 

own 2012 gross heat rate. Therefore, if a coal-fired EGU employed better practices for 

maintenance and operation in the past than it did in 2012, as characterized by a lower gross heat 

rate, the agency believes it is reasonable to base the potential heat rate improvement for the 

population of EGUs in the interconnection region on their more efficient operations – whether for 

individual EGUs those operations were in 2012 or as early as 2002.45 This longer time period better 

reflects various operating practices and investment priorities for the sector. As one power-sector 

trade magazine stated, “[R]elatively cheap coal and a focus on plant emissions controls has, 

unfortunately, taken the focus away from maintaining and improving plant heat rate.”46 EPA’s 

analysis seeks to identify the potential for EGUs to re-achieve their better heat rates. 

Commenters also noted that the state goals are based on CO2 intensity using net electricity 

generation, but EPA based its potential heat rate improvement analysis on gross heat rate.47 The 

commenters suggested that EPA should change the analysis to use net heat rate. While EPA 

acknowledges the discrepancy between the unit of measure for the state goals and the gross heat 

rate improvement analysis, the agency uses hourly gross heat rate for the analysis because it is the 

only comprehensive, quality assured hourly dataset available to the agency. The agency also 

believes the use of gross heat rate is inherently conservative for two reasons: (1) it does not reflect 

additional opportunities that coal-fired EGUs may have to reduce auxiliary energy use (i.e.,

parasitic load), 48 and (2) gross electricity generation is not affected by auxiliary energy use 

required to power the significant capacity of air pollution control devices that were installed 

between 2002 and 2012. If net heat rate data were available and the agency used these data for the 

analytical approaches described below, the auxiliary energy use of air pollution control devices 

installed during the study period could possibly inflate the potential heat rate improvement. 

2.5.2 Influence of ambient conditions and capacity factor on gross heat rate 

Ambient conditions can affect a coal-fired EGU’s gross heat rate. EPA evaluated three ambient 

conditions – hourly capacity factor, hourly air temperature, and, at the suggestion of commenters, 

hourly surface water temperature – to assess the influence these conditions can have on coal-fired 

EGU’s gross heat rate. The assessment results indicate that each of the ambient conditions has 

 

45 Beginning in reporting year 2002, the agency introduced numerous quality assurance checks on hourly emission 

data, enhancing the data quality. Therefore, for the purpose of the potential heat rate improvement analyses, the agency 

chose not to include data prior to 2002. 

46 See U Nowling, 2015. “Understanding coal power plant heat rate and efficiency,” Power. Available at 

http://www.powermag.com/understanding-coal-power-plant-heat-rate-and-efficiency. 

47 For an explanation of why EPA used gross heat rate in this heat rate improvement analysis, section V.C.3 of the 

final rule preamble of the CPP. 

48 See EPRI, 2011. Opportunities to Enhance Electric Energy Efficiency in the Production and Delivery of Electricity. 

Technical Report 1024651. Available at 

http://www.pserc.wisc.edu/documents/publications/special_interest_publications/EPRI_Electricity_Use_Report_Fin

al_1024651.pdf. The report explores the auxiliary electricity consumption at EGUs and finds that auxiliary energy use 

(and transmission and distribution losses) can be reduced by 15 percent through available technology options, 

including variable speed drives. 
2 – 34 

significant influence on the variation in hourly gross heat rate of the 884 coal-fired EGUs during 

the study period (2002–2012). The agency chose to control only for the influence of hourly 

capacity factor and hourly ambient air temperature in two of the three analyses described below 

because: (1) comprehensive water temperature data from monitoring locations nearby and 

upstream of each of the 884 coal-fired EGUs were not available, and (2) water temperature data 

are highly correlated with ambient air temperature data so, as described below, the additional 

influence of water temperature on heat rates is implicitly included. 

2.5.2.1 Capacity factor 

Coal-fired EGUs are designed to operate most efficiently near full capacity. In general, as an 

EGU’s hourly capacity factor declines, the EGU’s hourly gross heat rate increases. Across the full 

range of capacity factors, this trend is not linear. This relationship between hourly gross heat rate 

and capacity factor is illustrated in Figure 2-6. Each dot represents the average hourly gross heat 

rate for all measurements at a specific hourly capacity factor for four “sister units”49 in 2012. The 

bottom chart shows the number of hourly measurements at each hourly capacity factor. The figure 

illustrates three effects that are broadly applicable to the 884 coal-fired EGUs: (1) average hourly 

gross heat rate is higher and shows more variation, or “scatter,” from the average at lower hourly 

capacity factors, (2) the gross heat rate trend flattens at around 60 – 70 percent hourly capacity 

factor, and (3) the majority of operating hours are at higher hourly capacity factors. 

Figure 2-6 Average hourly gross heat rates at partial load levels 

Notes: 2012 data are from four 650 MW coal-fired “sister units” at JM Stuart in Ohio; 

cumulative number of measurements exceeds 8,784 hours because it includes measurements 

from four EGUs; the trend line is a power function in the form of y=c×xb

; average hourly gross 

heat rate axis does not start at zero to better illustrate changes in gross heat rate 

 

49 Four 650 MW cell burner coal-fired EGUs at J M Stuart Station in Ohio. 
2 – 35 

For each EGU, EPA used a least-squares linear regression to assess the relationship between hourly 

gross heat rates and hourly capacity factor.50 The hourly capacity factor explained about 18 percent 

of the variation in gross heat rate for the 884 coal-fired EGUs between 2002 and 2012. These 

results, however, conceal considerable variability. At some coal-fired EGUs hourly capacity factor 

has a greater influence on hourly gross heat rate. For example, hourly capacity factor can explain 

nearly 50 percent of variation in gross heat rate at some load-following EGUs. The analysis 

indicates that as EGUs move from base load to load following (i.e., cycling operation), hourly 

capacity factor tends to have a larger influence on gross heat rate. 

Several commenters noted that the power sector saw dramatic changes during the 11-year study 

period, including significant declines in annual capacity factors at coal-fired EGUs. As discussed 

in the proposal TSD, it can be difficult to interpret the relationship between annual capacity factors

and hourly capacity factors as used in the agency’s analyses. EGUs with high annual capacity 

factors (e.g., greater than 90 percent) must operate at high levels for most hours in a year. However, 

it is not possible to make such a generalization at lower annual capacity factors. For example, if a 

hypothetical coal-fired EGU operated at 90 percent capacity for six months and was offline for the 

remaining six months of the year, the EGU’s annual capacity factor would be only 45 percent – 

equivalent to an EGU that might have operated below 50 percent capacity factor for the entire year. 

However, to assess the commenters’ concerns, the agency reviewed two annual capacity factors 

for the 884 coal-fired EGUs – annual capacity factor and annual capacity factor during hours of 

operation. The latter represents the average annual capacity factor for the 884 coal-fired EGUs 

excluding hours in which the EGU did not operate, such as downtime for maintenance or due to 

low electricity demand. This latter capacity factor reflects the average capacity levels when EGUs 

are operating.

As shown in Table 2-3 and Figure 2-7, the annual average capacity factor of the 884 coal-fired 

EGUs declined by approximately 10 percent in 2009 and, after a small increase in 2010, continued 

to decline through 2012. However, the annual capacity factor during operating hours (i.e.,

excluding non-operating hours) declined after 2010, but by a much smaller amount. This illustrates 

that during the 11-year study period, coal-fired EGUs operated fewer hours in later years, but 

maintained high hourly capacity factors, on average, during the hours they operated. 

 

50 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R2

) values 

were weighted using the EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted R2

values were averaged across all 884 coal-fired EGUs, resulting in an R2

 value of 0.179. 
2 – 36 

Table 2-3 Annual capacity factors for 884 coal-fired EGUs 

Year Annual capacity 

factor (%) 

Operating hours 

capacity factor (%) 

2002 68 77 

2003 69 79 

2004 70 79 

2005 71 80 

2006 70 80 

2007 71 81 

2008 70 80 

2009 62 77 

2010 65 78 

2011 61 76 

2012 53 72 

Figure 2-7 Change in annual capacity factors for 884 coal-fired EGUs relative to 2002 

Note: capacity factors are relative to 2002 capacity factor, the first year of the study period 
2 – 37 

2.5.2.2 Ambient air temperature 

Ambient air temperature can affect gross heat rate in three ways: (1) ambient air (primary air) is 

mixed with fuel prior to combustion and a fraction of energy released during combustion heats the 

incoming air, (2) in many regions of the country temperature affects electricity demand and, as a 

result, some EGUs’ hourly capacity factors may increase when ambient temperatures are high, and 

(3) ambient air temperature influences cooling water temperatures, affecting an EGU’s efficiency. 

The relationship between hourly ambient air temperature and hourly capacity factor is complex. 

Generally, peak electricity demand in most parts of the U.S. occurs on the hottest days of the year. 

However, each EGU responds differently depending on design, duty cycle, meteorological 

conditions, electricity demand, and other factors. For example, a baseload plant may operate at 

high hourly capacity factors regardless of temperature. EPA assessed the correlation between 

hourly ambient air temperature and hourly capacity factor during the study period (2002–2012) to 

determine and found the correlation to be very low.51

For each EGU, EPA used a least-squares linear regression to assess the influence that hourly 

ambient air temperature has on the variability of hourly gross heat rates.52 The hourly ambient air 

temperature explained about 8 percent of the variation in gross heat rate across the entire fleet. At 

approximately one-fourth of the 884 coal-fired EGUs ambient air temperature had a greater 

influence on hourly gross heat rate than the influence of hourly capacity factor. At some individual 

EGUs, ambient air temperature may explain up to 30 percent of the variation in hourly gross heat 

rate. These are typically, but not exclusively, units with freshwater once-through cooling systems. 

Identifying temperature-responsive coal-fired EGUs allows us to understand why heat rate may 

increase during periods of peak demand. These are the EGUs where the ambient temperature 

‘signal’ is an important variable. Gross hourly heat rates at a temperature-responsive EGU may 

increase as much 10 percent during high temperature days in summer months. 

2.5.2.3 Cooling water temperature 

As discussed earlier in this chapter and noted by commenters, cooling water temperature can have 

a significant influence on a coal-fired EGU’s heat rate. The availability of a cold heat sink in the 

condenser is important to maximize the EGU’s efficiency. The design of the heat exchanger, type 

of cooling system, availability of water, and temperature of water all have an impact on 

performance. Higher water temperatures typically reduce the effectiveness of the cooling system 

and, therefore, overall plant efficiency. 

 

51 The Pearson correlation coefficient for hourly ambient air temperature and hourly capacity factor from 2002–2012 

is -0.048. 

52 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R2

) values 

were weighted using the EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted R2

values were averaged across all 884 coal-fired EGUs, resulting in an R2

 value of 0.083. 
2 – 38 

Commenters suggested EPA should account for the influence of cooling water temperature in 

addition to ambient air temperature. The agency did not have comprehensive cooling water 

temperature data to assess the influence on gross heat rate at the time of the proposal. However, in 

response to commenters’ suggestions, the agency compiled hourly surface water temperature data 

for 45 coal-fired EGUs in the Eastern Interconnection. For each EGU and each year with available 

data, EPA used a least-squares linear regression to assess the influence that hourly water 

temperatures had on the variability of hourly gross heat rates.53 The variation in hourly water 

temperature explained about 7 percent of the variation in gross heat rate. 

When EPA applied the same approach for the 45 coal-fired EGUs over the same time period using 

hourly ambient air temperature instead of hourly water temperature, the air temperatures explained 

approximately 6 percent of the variation in gross heat rate.54 The agency also examined the 

influence of several time-averaged temperatures (air and water temperatures) and found 

comparable results to the hourly ambient temperature values. For example, the rolling 24-hour 

water temperatures and rolling 24-hour ambient air temperatures explained 7 percent of the 

variation in hourly gross heat rate.55 Not surprisingly, the air temperature and water temperature 

are strongly related.56

Although the surface water data in this analysis were from the Eastern Interconnection, where 86 

percent of the 884 coal-fired EGUs are located, the agency has no reason to believe, nor have 

commenters suggested, that this correlation would not hold elsewhere in the country. Studies 

suggest that ambient air temperature is similarly correlated with surface water temperature 

elsewhere in the country. See, for example, a report by the New Mexico Environment Department 

notes that “based on data for approximately 300 New Mexico streams, air temperature is highly 

correlated with stream water temperature...”57; Kaushal et al. found “[l]ong-term increases in 

stream water temperatures were typically correlated with increases in air temperatures”;58 and 

Webb et al. found there is usually a strong correlation between ambient air temperatures above 0° 

 

53 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R2

) values 

were weighted using each EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted R2

values were averaged across all 45 EGUs, resulting in an R2

 value of 0.072. 

54 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R2

) values 

were weighted using each EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted R2

values were averaged across all 45 EGUs, resulting in an R2 value of 0.057. 

55 The R2

 value for rolling 24-hour water temperature and hourly gross heat rate was 0.071 and the R2 for rolling 24-

hour ambient temperature and hourly gross heat rate was 0.072. 

56 The Pearson correlation coefficient for hourly water temperature and 24-hour ambient air temperature is 0.74. 

57 See New Mexico Environment Department, 2011. Air-Water Temperature Correlation. Available at 

https://www.env.nm.gov/swqb/documents/swqbdocs/MAS/Protocols/Air-Water/Air-Water08-01-2011.pdf. 

58 See SS Kaushal, GE Likens, NA Jaworski, ML Pace, AM Sides, D Seekell, KT Belt, DH Secor, and RL Wingate, 

2010. “Rising stream and river temperatures in the United States,” Frontiers in Ecology and the Environment. 8:461-

466. (DOI: 10.1890/090037). 
2 – 39 

C and stream water temperatures (although the relationship may be non-linear at higher 

temperatures).59

Based on these results, the agency believes that hourly ambient air temperature is the appropriate 

variable to use in the potential heat rate improvement analyses because: (1) available hourly water 

temperature data are limited to a portion of the fleet, (2) air and water temperatures are highly 

correlated (i.e., simultaneously controlling for air temperature and water temperature appears 

highly redundant), and (3) the results from all of the agency’s analyses described above 

demonstrate that ambient air temperature and water temperature explained similar amounts of the 

variability in hourly gross heat rate. 

2.5.2.4 Capacity factor and ambient air temperature 

In addition to the regression analyses discussed above, EPA conducted a least-squares linear 

regression analysis to assess the influence that the combination of hourly capacity factor and 

hourly ambient air temperature had on the variability of hourly gross heat rate.60 For the 884 coalfired EGUs, the combination of hourly capacity factor and hourly ambient air temperature 

explained about 26 percent of variation in gross heat rate between 2002 and 2012. 

2.5.2.5 Accounting for the influence of factors outside coal-fired EGU operators’ control 

The objective of EPA’s analysis is to identify the potential heat rate improvements at coal-fired 

EGUs for the purpose of calculating state goals. Therefore, the agency attempted to control for 

many of the factors that influence gross heat rate but are beyond the control of the coal-fired EGU 

operator. For example, as stated above, EPA’s analysis controls for EGU design characteristics 

such as boiler type and size, generator size, coal type, and cooling system type because it compares 

each coal-fired EGU’s historical performance against the EGU’s own 2012 performance. Because 

these design characteristics are typically constant over time, they do not influence the results of 

the EGU-level analyses. As mentioned earlier, age does not appear to have a significant effect on 

gross heat rate. In addition, EPA controlled for the installation of air pollution control devices by 

analyzing gross heat rate – because the impact of air pollution control devices’ auxiliary energy 

consumption does not affect gross electricity generation, the addition of controls does not influence 

the results of the gross heat rate analyses. 

In two of the three analytical approaches discussed below, EPA also controlled for hourly capacity 

factor and hourly ambient air temperature because these two environmental and operating factors 

are considered to be beyond the EGU operators’ control. To limit the influence of these two factors, 

 

59 See BW Webb, DM Hanna, RD Moore, LE Brown and F Nobilis, 2008. “Recent advances in stream and river 

temperature research,” Hydrological Processes. 22: 902-918 (DOI: 10.1002/hyp.6994). 

60 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R2

) values 

were weighted using the EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted R2

values were averaged across all 884 coal-fired EGUs, resulting in an R2

 value of 0.255. 
2 – 40 

the agency grouped each EGU’s hourly gross heat rate values into unit-specific “capacitytemperature bins” based on hourly capacity factor and hourly ambient air temperature. This 

enabled the agency to compare gross heat rate values for each coal-fired EGU during hours with 

similar capacity factor and ambient air temperature conditions, effectively controlling for a large 

influence on the variation in an EGU’s hourly gross heat rates. 

To create the capacity-temperature bins, the agency divided the range of hourly capacity factors 

into 12 groups, each separated by 10 percent of an EGU’s electricity generating capacity (i.e., 0-

9, 10-19, 20-29, ... , 90-99, 100-109, greater than or equal to 110 percent), and the ambient air 

temperatures into 14 groups, each separated by 10° F (i.e., below -

10°, -10--1°, 0-9°, ... , 90-99°, 

100-109°, greater than or equal to 110° F). The result is 168 capacity-temperature bins (12 X 14) 

for each of the 884 coal-fired EGUs. However, not all capacity-temperature bins for an individual 

EGU have data (e.g., coal-fired EGUs in Florida are unlikely to experience hourly ambient 

temperatures below -10° F). 

At least one commenter expressed concern that the hourly capacity factor ranges were too large. 

They pointed to information that showed heat rate can vary significantly at low hourly capacity 

factors, resulting in differences of as much as 4 percent from a 30 percent hourly capacity factor 

to a 40 percent hourly capacity factor. Figure 2-6 illustrates this concept. At higher hourly capacity

factors the hourly gross heat rate declines. However, the trend is more pronounced at low capacity 

factors, as noted by the commenter, and less prominent at higher capacity factors. EPA chose to 

maintain the size of the hourly capacity factor ranges for three reasons: (1) approximately 70 

percent of the hourly values are greater than 70 percent capacity factor in each of the three 

interconnection regions and nationally (see Figure 2-8), (2) the data are skewed toward the higher 

end of each range (e.g., more hourly data points range from 75-79 percent range than from 70-74 

percent), and (3) reducing the range of the hourly capacity factor groups would double the number 

of capacity-temperature bins, resulting in fewer measurements in each bin and more data excluded 

from the analysis.61 EPA’s assessment does not indicate that using a smaller range for capacity 

factor would have a significant impact on the results. 

 

61 As described below, in the efficiency and consistency analysis EPA excluded any capacity-temperature bins that 

had fewer than 15 hourly measurements for a single coal-fired EGU. 
2 – 41 

Figure 2-8 Distribution of hourly capacity factors by interconnection 

Several commenters noted that EPA’s analysis does not account for all the factors that affect a 

coal-fired EGU’s heat rate. Specifically, these commenters mention the influence of geographic 

differences (e.g., seasonality, altitude), coal rank and quality, equipment degradation, and 

operating conditions. As stated above, the agency made every effort to control for design 

characteristics and ambient conditions that the agency believes to be outside the EGU operator’s 

control. The agency also controlled for capacity factor, which some commenters argued is partially 

under the operator’s control. However, the agency lacks sufficient information to determine the 

portion of hourly capacity factor that is under the direct control of influence of the EGU owner or 

operator. Therefore, the agency chose to reasonably and conservatively control for the full effect 

of hourly capacity factor based on the results of the analysis described above. Regarding 

commenters’ assertion about the role of a coal-fired EGU’s location (i.e., geography) on heat rate, 

the agency believes that the approaches described below do control for these conditions because 

coal-fired EGUs are not considered mobile sources so by comparing each EGU’s performance 

from 2002 to 2012 against the EGU’s performance in 2012 the agency controlled for both seasonal 

differences and location characteristics (e.g., altitude). Commenters also noted that coal-fired 

EGUs that changed coal rank during the study period might have significantly different heat rates 

depending on the rank of coal burned. The agency recognizes that: (1) different coal ranks have 

different heating values and moisture content, and (2) coal-fired EGUs are generally designed for 

a specific coal rank and coal quality and deviations from that coal rank can adversely affect heat 

rate. However, the agency believes that for most coal-fired EGUs coal rank and coal quality are 

controlled by the EGU operator through purchase contracts with fuel suppliers. Therefore, the 

agency chose not to control for coal rank or coal quality. Commenters also stated that equipment 

degrades over time, increasing heat rate. As discussed further in section 2.6.8 below, EPA 

recognizes that all equipment will degrade over time. However, some of the best practices that can 
2 – 42 

be employed to improve heat rate involve properly operating and maintaining equipment to 

minimize the degradation and, when necessary, replacing equipment to maintain efficient 

operation of the plant. Therefore, the agency did not control for the effect of equipment degradation 

in the analyses described below because EGU operators can limit equipment degradation through 

best practices for operation and maintenance.62 Similarly, the agency believes operating conditions 

at each coal-fired EGU are largely under the control of the EGU operator so it is reasonable and 

appropriate for the agency to include variability due to operating conditions (with the exception of 

capacity factor.) 

2.5.3 Heat rate improvement approach “best historical performance” 

In the proposal TSD, EPA included an assessment of potential heat rate improvement based on the 

difference between each coal-fired EGU’s best rolling annual gross heat rate and its 11-year (2002–

2012) gross heat rate. Based on commenters’ suggestions and heat rate improvement analyses 

provided by commenters, the agency refined the approach and calculated 1- and moving 2-year 

average gross heat rates for each of the 884 coal-fired EGUs in the study for the years 2002–2012. 

The agency removed the 3-year averaging period discussed in the proposal TSD because, as noted 

by commenters, the differences in temperature profiles and loads from one year to the next are 

relatively small and not large enough to justify a 3-year rolling average. Commenters suggested 

that 1 year is sufficient because the agency already controls for capacity factor and temperature in 

some of the analytical approaches. However, the agency chose to conservatively include a 2-year 

rolling average to account for atypical annual operations due to factors such as extended outages, 

equipment failures, cooling water availability, or unusual demand patterns. The advantage of using 

the moving 2-year average is that it can smooth out the peaks and troughs and reduce the influence 

of a single unusual year (e.g., low utilization, unusual weather or climate conditions), while not 

diluting the results so much that it would obscure significant changes to annual gross heat rate. 

The agency selected each EGU’s best (i.e., lowest) 1- and moving 2-year average gross heat rates, 

calculated the generation-weighted average across all EGUs in each interconnection, and 

compared the results to the interconnection’s generation-weighted 2012 average gross heat rate. 

As described above, this approach controls for design characteristics that remain constant and is 

not affected by the installation and operation of air pollution control devices. 

2.5.3.1 Methodology 

The procedures for calculating the potential heat rate improvement are: 

1. For each coal-fired EGU, calculate the 1- and moving 2-year average gross heat rate. 

2. Identify each EGU’s best 1- and moving 2-year gross heat rate. 

 

62 See section 2.6.8 for more information about equipment degradation and maintaining heat rate performance. 
2 – 43 

3. Calculate the generation-weighted average heat rate based on the lowest 1- and moving 

2-year average gross heat rate and the generation over the time period within each 

interconnection region. 

4. For each interconnection, calculate the 2012 average gross heat rate weighted by the 

2012 generation. 

5. For each interconnection, calculate the potential heat rate improvement – the percent 

difference between the best 1- and moving 2-year average gross heat rate from step 3 

and the 2012 gross heat rate from step 4. 

2.5.3.2 Results 

This approach identifies the potential heat rate improvement by determining what would have been 

possible if each of the 884 coal-fired EGUs had reproduced its demonstrated best performance 

from 2002 to 2012. These results are conservative because they only account for a return to each 

EGU’s historical best annual performance and do not account for the full extent of heat rate 

improvement available through additional equipment upgrades and best practices. The results of 

the analytical approach are presented in Table 2-4.

Table 2-4 Potential heat rate improvement based on 

best historical performance, by interconnection 

Performance period Western Texas Eastern Nationally63

Best 1-year gross heat rate (%) 4.1 4.2 6.3 5.9 

Best moving 2-year gross heat rate (%) 2.6 3.1 4.9 4.6 

2.5.4 Heat rate improvement approach “best historical performance under similar 

conditions” 

The second analytical approach – best historical performance under similar conditions – is an 

outgrowth of the first and third analytical approaches based on comments and analyses provided 

by commenters. The second approach is similar to the first approach described above, but in this 

approach EPA controlled for each EGU’s hourly capacity factor and hourly ambient air 

temperature as described above. Within each capacity-temperature bin for each coal-fired EGU, 

EPA calculated the bin-specific 1-year and moving 2-year average gross heat rates, selected the 

lowest gross heat rate within that capacity-temperature bin, calculated the generation weighted 

average across all bins and units, and compared that to the generation weighted 2012 average gross 

 

63 EPA has provided nationwide results for purposes of comparison; they are not part of the methodology.
2 – 44 

heat rate. One advantage of this approach is that it controls for factors that explain a significant 

portion of variation in EGUs’ gross heat rate. 

2.5.4.1 Methodology 

The procedures for calculating the potential heat rate improvement using this approach are the 

same as those shown above with one exception: the agency partitioned the hourly data by hourly 

capacity factor and temperature as described in the following steps. 

1. For each coal-fired EGU and capacity-temperature bin, calculate the 1- and moving 2-

year average gross heat rate. 

2. Identify each EGU’s best 1- and moving 2-year gross heat rate in each temperature and 

capacity factor bin. 

3. Calculate the generation-weighted average heat rate using the lowest 1- and moving 2-

year average gross heat rates from step 2 and the generation over the time period for all 

the EGUs and temperature and capacity factor bins in each interconnection region 

4. Within each interconnection, calculate the 2012 average gross heat rate weighted by the 

2012 generation. 

5. For each interconnection, calculate the potential heat rate improvement – the percent 

difference between the best 1- and moving 2-year average gross heat rate from step 3 and 

the 2012 gross heat rate from step 4. 

2.5.4.2 Results 

This approach identifies the potential heat rate improvement by determining what would have been 

possible if each of the 884 coal-fired EGUs had reproduced its demonstrated best performance 

from 2002 to 2012 while controlling for the influence of hourly capacity factor and hourly ambient 

temperatures. These results are conservative because they account only for potential heat rate 

improvements if each coal-fired EGU returned to its best performance; it does not account for the 

full extent of heat rate improvements available through additional equipment upgrades and best 

practices. 

 One reason this approach yields interconnection-level results that are higher than the first 

approach is that it relies on the lowest 1- and moving 2-year average gross heat rates within each 

capacity-temperature bin. Therefore, unlike the first approach where the “best” performance for 

each coal-fired EGU is from one or two years across all capacity factor and temperature conditions, 

this second approach may have different “best” years for the different capacity-temperature bins. 

The results of this second analysis are shown in Table 2-5. 
2 – 45 

Table 2-5 Potential heat rate improvement based on 

best historical performance under similar conditions, by interconnection 

Performance period Western Texas Eastern Nationally64

Best 1-year gross heat rate (%) 4.7 4.9 6.9 6.6 

Best moving 2-year gross heat rate (%) 3.1 3.5 5.3 5.0 

2.5.5 Heat rate improvement approach “efficiency and consistency under similar 

conditions” 

The two approaches described above are based on the general principle that coal-fired EGUs are 

capable of returning to historically demonstrated heat rates (e.g., best annual average over a 1- or 

moving 2-year period). The third approach, which was described extensively in the proposal TSD, 

uses a different approach to determine the potential heat rate improvement. This third approach is 

based on the principle that coal-fired EGUs are capable of operating more consistently with their 

historically demonstrated “best” hourly gross heat rates. For this approach, the agency grouped 

each coal-fired EGU’s data by capacity-temperature bin and determined a 1- and moving 2-year 

gross heat rate “benchmark.” 65 This benchmark was based on each coal-fired EGU’s 10th 

percentile hourly gross heat rate during each 1- and moving 2-year period for each capacitytemperature bin. The 10th percentile value means 10 percent of all hourly values for the 1- or 2-

year period were below the selected value (i.e., the benchmark is demonstrated by 10 percent of 

all measurements in each capacity-temperature bin). The agency chose the 10th percentile because 

it represents a demonstrably achievable gross heat rate indicating efficient operation of the EGU, 

but ignores unusually low outlier values.66 Furthermore, the 10th percentile is a commonly used 

statistical level for determining if particular values are unusually low relative to the average 

value.67 

In this approach, EPA assumed that a coal-fired EGU could achieve the benchmark with the same 

frequency in the future as in the past while also assuming that for the remaining time it could 

achieve heat rates that were closer to the benchmark under similar hourly capacity factor and 

hourly ambient air temperature conditions. The agency calculated the potential heat rate 

 

64 EPA has provided nationwide results for purposes of comparison; they are not part of the methodology.

65 EPA excluded capacity-temperature bins that had fewer than 15 hourly measurements for a single coal-fired EGU. 

66 During the development of the dataset the agency excluded extreme outliers – hourly gross heat rate values that 

were greater than ±2.6 standard deviations from the EGU’s mean gross heat rate. 

67 Common statistical levels include the 10th and 90th percentiles, the 5th and 95th percentiles, and the 1st and 99th 

percentiles. EPA considered these other levels but selected the 10th percentile because it is a common statistical level, 

is conservative, and is substantially less likely to have been an outlying value than the 5th or 1st percentile. If EPA 

had selected the 5th percentile as the benchmark, each EGU would have half as many hours of historical operation at 

or below the benchmark and the overall resulting potential heat rate improvement would have been substantially larger. 
2 – 46 

improvements from greater consistency (i.e., less variability of heat rate) such that each EGU’s 

hourly gross heat rates that were greater than the benchmark for the capacity-temperature bin were 

adjusted downward by a specific percentage – a consistency factor – to represent more consistently 

efficient operation of the EGU. Using this approach, the hourly gross heat rate values that were 

adjusted most in absolute terms were the highest values (i.e., less-efficient hours of operation) that 

indicate a relative failure to fully employ heat rate improvement measures. The result is that the 

post-adjustment distribution of hourly heat rate values has a smaller variability in heat rate, with 

the average value nearer the benchmark value, while the lowest heat rate values remain unchanged 

(see Figure 2-9). This is based on the principle that a coal-fired EGU following best practices 

should be able to consistently operate closer to the demonstrated and achievable benchmark heat 

rate. 

Figure 2-9 Frequency of hourly gross heat rates measured and adjusted, 2002–2012 for an 

example coal-fired EGU in a single capacity-temperature bin 

In the proposal TSD, EPA assessed several different consistency factors ranging from 10 to 50 

percent and proposed 30 percent because the results were similar to results achieved through other 

analytical approaches (e.g., 3-year rolling averages relative to the 11-year average), accorded with 

EPA experts’ engineering judgment, and because 30 percent was in the middle of the reasonable 

range evaluated by the agency. Several commenters suggested that this choice of values was 

arbitrary and could have easily been higher or lower had EPA selected the middle of a different 

range of values. To identify a consistency factor with more precision for this analytical approach, 

EPA analyzed EGUs’ historically demonstrated level of consistency (i.e., variability) for hourly 

gross heat rate over 1-year and moving 2-year periods between 2002 and 2012 for each capacitytemperature bin. The agency calculated the mean-centered standard deviation of hourly gross heat 

rate values for each 1- and 2-year period for the population of EGUs in each interconnection for 
2 – 47 

all capacity-temperature bins.68 The lowest standard deviations – a measure of variability – for the 

1- and moving 2-year periods were then compared to the mean-centered standard deviation of 11-

year gross heat rate data. The percent difference of the 1- and moving 2-year standard deviations 

and the 11-year standard deviation represent the potential consistency improvement at each 

interconnection. In other words, this approach compares the EGUs’ most consistent performance 

(i.e., least variability) for each capacity-temperature bin over a 1- or moving 2-year period to the 

EGUs’ average consistency (i.e., variability) over the study period (2002-2012). 

As described above at the beginning of section 2.5, EPA replaced the 11-year baseline used in the 

proposal TSD with a 2012 baseline in this analysis. For the population of EGUs in each 

interconnection, the refined approach – compares the generation weighted average heat rate across 

all bins and units adjusted to be “efficient and consistent”, with the generation weighted 2012 

average gross heat rate. 

Several commenters noted that reduced variability does not necessarily equate to improved heat 

rates. EPA agrees with commenters that reducing variability alone does not result in lower heat 

rates and, therefore, lower CO2 emissions. For example, if an EGU were to operate more 

consistently at a higher heat rate (i.e., less efficient), the variability would decrease, but the heat 

rate would increase. However, the agency is not simply asking for less variability in operation at 

existing coal-fired EGUs. Instead, the agency is assessing the average heat rate improvement that 

would result if coal-fired EGUs had operated more consistently nearer their gross heat rate 

benchmarks. Both the benchmark gross heat rates and reduced variability in heat rate were 

demonstrated by the EGUs from 2002–2012 during hours with similar hourly capacity factors and 

hourly ambient air temperatures. 

 

 

68 The mean-centered standard deviation shows the extent of variation relative to the mean and allows for meaningful 

comparison of variability from coal-fired EGUs that can have different average gross heat rates. The mean-centered 

standard deviation, also called the coefficient of variation, is calculated by dividing each coal-fired EGU’s standard 

deviation for the capacity-temperature bin by the EGU’s capacity-temperature bin’s average gross heat rate. It is 

reasonable to use the standard deviation when evaluating the potential to reduce variability because standard deviation 

is the most common measure of the spread, or variation, of values within a dataset. 
2 – 48 

2.5.5.1 Methodology 

The procedures for calculating the consistency factor are: 

1. For each coal-fired EGU, calculate the 1-year average gross heat rate for each 

capacity-temperature bin.69

2. For each coal-fired EGU, calculate the difference, as a percentage, between every 

hourly gross heat rate value and the value from step 1 within each capacitytemperature bin. 

3. Within each interconnection, calculate the mean-centered deviation of all the values 

derived from step 2 for each 1-, moving 2-, and 11-year period.70

4. Within each interconnection, calculate the consistency factor as the percent difference 

between the lowest 1- and 2-year standard deviations and the 11-year standard 

deviation from step 3. 

The procedures for calculating the potential heat rate improvement are: 

1. For each coal-fired EGU, determine the gross heat rate benchmarks – the 10th percentile 

hourly gross heat rate – for each 1- and moving 2-year period within each capacitytemperature bin. 

2. Identify each EGU’s best 1- and moving 2-year gross heat rate benchmark within each 

capacity-temperature bin. 

3. For each hourly heat rate value, calculate the adjusted gross heat rate: 

a. For each hourly gross heat rate value greater than the 1- or moving 2-year 

benchmark calculate the difference between the hourly value and the relevant 

benchmark; 

b. Multiply the difference by the relevant 1- or moving 2-year interconnectionspecific consistency factor; and 

c. Subtract the result from the hourly gross heat rate value. 

4. Within each interconnection, calculate the 11-year generation-weighted adjusted 

average gross heat rate representing “efficient and consistent” operation using the 

adjusted gross heat rate values from step 3. 

5. For each interconnection, calculate the 2012 average gross heat rate weighted by the 

2012 generation. 

 

69 The agency did not calculate 2-year average gross heat rates because any change in average heat rate from one year 

to the next could lead to overestimates in the amount of variability. The method used here accounts for changes in 

average heat rate from one year to the next, so that only the relative variability is being evaluated.

70 This approach identifies the variability of the best 1 or 2-year time period relative to the 11-year average variability. 

As described above, mean-centering the data accounts for changes in heat rate over the time period. Because the 

analysis accounts for changes in heat rate, the average variability is more-representative than the variability of any 

particular year (e.g., 2012). 
2 – 49 

6. For each interconnection, calculate the generation-weighted potential heat rate 

improvement by calculating the percent difference between the 11-year adjusted heat 

rate average from step 4 and the 2012 generation-weighted gross heat rate from step 5. 

One commenter noted that EPA’s analysis in the proposal TSD used heat input instead of gross 

heat rate to assess potential heat rate improvements. The commenter noted that EPA’s assumption 

of an equivalent level of gross electricity generation across the full range of the capacity factor bin 

skewed the results. EPA agrees with the commenter that using the gross heat rate for the analysis 

provides more accurate results. Therefore, the agency changed the approach to use hourly gross 

heat rate, calculated as heat input divided by gross electricity generation. 

2.5.5.2 Results 

This approach calculates the potential heat rate improvement by determining what would be 

possible if each of the 884 coal-fired EGUs had operated more efficiently and consistently nearer 

their historically demonstrated “best” gross heat rate from 2002 to 2012 while controlling for the 

influence of hourly capacity factor and hourly ambient temperatures. The consistency factors 

calculated for this analytical approach are presented in Table 2-6 and the potential heat rate 

improvement results are presented in Table 2-7. 

Table 2-6 Consistency factors based on best historical performance 

under similar conditions, by interconnection 

Performance period Western Texas Eastern Nationally71

Best 1-year consistency (%) 46.9 46.6 45.5 45.8 

Best 2-year consistency (%) 38.4 37.1 38.1 38.2 

 

Table 2-7 Potential heat rate improvement based on efficiency and 

consistency under similar conditions, by interconnection 

Performance period Western Texas Eastern Nationally72

Best 1-year benchmark and consistency (%) 3.5 3.7 5.6 5.3 

Best 2-year benchmark and consistency (%) 2.1 2.3 4.3 4.0 

 

71 EPA has provided nationwide results for purposes of comparison; they are not part of the methodology.

72 EPA has provided nationwide results for purposes of comparison; they are not part of the methodology.
2 – 50 

2.5.6 Summary of results 

Each of the three analytical approaches described above constitutes a reasonable means of 

assessing the potential for heat rate improvements by affected coal-fired EGUs within the regional 

interconnections. Because the analyses are based on actual historical gross heat rate data for each 

of the 884 coal-fired EGUs in the study population, EPA is confident that the potential heat rate 

improvements identified on the regional basis through these approaches are feasible. 

Although each of these three analytical approaches is a reasonable definition of heat rate 

improvement potential under building block 1, EPA’s overall methodology adopts the most 

conservative value identified by any of the approaches. Under each of the three analytical 

approaches, EPA separately calculated potential heat rate improvements based on the best 1-year 

and moving 2-year performance for each EGU in each region. In order to further increase our 

confidence in the region-wide results, EPA’s conservative methodology compared the six resulting 

values for each region and adopted the lowest value identified by any analytical approach. Because 

the results from the moving 2-year analyses will always be more conservative than those from the 

1-year analyses,73 and thus the 1-year analyses cannot provide the most conservative outcome as 

a whole, only the results of the moving 2-year analyses are presented in Table 2-8.74

Table 2-8 Potential heat rate improvement for best moving 2-year period, 

by interconnection 

Analytical approach Western Texas Eastern Nationally75

Best historical performance (%) 2.6 3.1 4.9 4.6 

Best historical performance under similar 

conditions (%) 

3.1 3.5 5.3 5.0 

Efficiency and consistency under similar 

conditions (%) 

2.1 2.3 4.3 4.0 

For the final rule, the “efficiency and consistency under similar conditions” approach provides the 

most conservative result for all three regions. Accordingly, the tripartite methodology adopts those 

results for purposes of calculating the relevant CO2 emission performance rates and accompanying 

 

73 The “best” gross heat rate over a moving 2-year period will always be higher than the best gross heat rate over a 1-

year period because the moving 2-year period includes at least one non-best year (i.e., a year with higher average gross 

heat rate values). 

74 The results of EPA’s inherently less conservative 1-year analyses can be found in section V.C.1.a of the final rule 

preamble of the CPP, as well as in the discussion above. 

75 EPA has provided nationwide results for purposes of comparison, they are not part of the methodology.
2 – 51 

state goals.76 Specifically, a well-supported and conservative estimate of the potential heat rate 

improvements (and accompanying reductions in CO2 emission rates) that affected coal-fired EGUs 

in the Western interconnection is 2.1 percent, in the Texas interconnection is 2.3 percent, and in 

the Eastern interconnection is 4.3 percent. The agency believes these values are conservative and 

that EGUs have demonstrated these gross heat rates during the study period. 

It is important to note that these potential heat rate improvements are based on the difference 

between coal-fired EGUs’ past performance and their 2012 performance; they do not reflect 

additional opportunities for heat rate improvements from best practices and equipment upgrades 

that could have been (and still can be) cost-effectively implemented at coal-fired EGUs. The 

agency decided not to pursue a separate heat rate improvement target for specific equipment 

upgrades due to (1) insufficient information about the current deployment of the technologies 

specified in the proposal TSD, (2) the availability of data necessary to conduct the statistical 

analyses to identify potential heat rate improvement, and (3) potential impact of past deployments 

of heat rate improvement technology at some EGUs that might already be reflected in the results 

of the analyses described above. 

2.6 Assessment of key comments 

EPA received more than 1,500 comments on building block 1 of the best system of emission 

reductions, including comments on the methodology and assumptions for calculating potential heat 

rate improvements. Several of the comments are addressed above and below, and in the preamble 

with the remaining comments addressed in the response to comments (RTC). EPA appreciates the 

information and insights that commenters shared about the proposal. The information assists the 

agency in developing a stronger, more effective rule. Below are several key comments related to 

potential heat rate improvement and the additional analyses conducted by EPA to respond to those 

comments. 

2.6.1 Effect of heat rate improvement implemented during study period 

Several commenters expressed concern that measures implemented at specific EGUs to improve 

heat rate between 2002 and 2012 would result in greater variability of heat rate and, therefore, 

inflate the potential heat rate improvements. EPA acknowledges variability from heat rate 

improvements would affect the results of the approach used for the proposal. However, by 

changing the methodology to use a 2012 baseline for the analytical approaches described in this 

chapter, any heat rate improvements made during the study period and maintained through 2012 

are accounted for and do not result in additional potential heat rate improvement. 

 

76 The “best historical performance” and “best historical performance under similar conditions” approaches identify 

greater potential heat rate improvement in each region. Accordingly, they inherently support the feasibility of the more 

conservative values identified by the “efficiency and consistency under similar conditions” approach. 
2 – 52 

2.6.2 Impact of air pollution controls installed during study period 

Commenters described many of the air pollution control devices that were installed at coal-fired 

EGUs between 2002 and 2012, noting that these devices can have significant auxiliary energy 

requirements (i.e., parasitic load) that reduce the amount of electricity available for distribution on 

the regional interconnection. As a result of lower net electricity generation, an EGU’s net heat rate 

increases. Many commenters recommended that EPA consider the auxiliary energy requirements 

of these controls when assessing the potential heat rate improvement. EPA recognizes that air 

pollution control devices have auxiliary power requirements and a significant amount of coal-fired 

EGUs were retrofitted with air pollution controls during the study period (2002-2012). However, 

EPA rejects the recommendation to credit the auxiliary power requirements in the analysis because 

the agency’s analysis is based on gross heat rate and, therefore, is not affected by auxiliary power 

requirements of air pollution controls.77

2.6.3 Impact of CO2 emissions from SO2 pollution control devices 

Commenters noted that the addition of wet flue gas desulfurization (WFGD, or wet scrubber) 

technologies result in increased process CO2 emissions because of the dissolution of limestone to 

capture SO2.

78 Because heat input, the numerator for calculating heat rate, is based on an EGU’s 

measured CO2 emissions, the additional process CO2 emissions result in artificial increases in 

calculated gross heat rate. Commenters suggest that process CO2 emissions can be “several 

percentage points.” To assess the range of potential impacts on EPA’s analysis from process CO2

emissions, the agency estimated the incremental CO2 from the WFGD chemical reaction. The 

results indicate that retrofit WFGD installed on existing coal-fired EGUs between 2002 and 2012 

increased 2012 CO2 emissions (and calculated heat input) by an average of no more than 0.7 

percent for those EGUs in 2012. 

To calculate the CO2 emissions, the agency first identified the 121 existing coal-fired EGUs that 

installed retrofit limestone WFGD between 2002 and 2012. Collectively these EGUs have 

approximately 59 GW of electricity generating capacity. Next, EPA reviewed monthly fuel 

transactions and calculated the weighted average sulfur content of fuel deliveries to the relevant 

power plants.79 Finally, the agency calculated the process CO2 emissions that could result at 85 

and 95 percent removal efficiencies (see Table 2-9). Due to data limitations – EGU-level fuel 

consumption data are not available – the agency assumed that: (1) coal delivered to the power plant 

in 2012 was representative of coal burned in the WFGD-equipped coal-fired EGU, and (2) EGUs 

operated the WFGD to remove 85 to 95 percent of the sulfur. The resulting process CO2 emission 

increase at coal-fired EGUs that installed retrofit limestone WFGD ranges from 0.2 percent (Texas) 

 

77 See supra note 47. 

78 The reaction in the WFGD absorber is summarized by SO2+CaCO3+1⁄2H2O → CaSO3+1⁄2H2O+CO2 (see RK 

Srivastava, 2000. Controlling SO2 emissions: a review of technologies. EPA report EPA/600/R-00/093. Available at 

http://nepis.epa.gov/Adobe/PDF/P1007IQM.pdf. 

79 Monthly fuel transactions are reported at the plant level on form EIA-923. 
2 – 53 

to 1.0 percent (Eastern). It should be noted, however, that this is a conservative estimate for the 

following reasons: (1) some of the sulfur is bound in pyrites and well-maintained coal 

pulverizers/grinders should remove much of the pyritic-bound sulfur, (2) a small amount of the 

combustion CO2 might be absorbed in the WFGD slurry, and (3) in 2012 some EGUs did not 

operate their WFGD at full capacity. 

Table 2-9 Increase in 2012 CO2 measurements due to 

retrofit limestone wet flue gas desulfurization 

Western Texas Eastern Nationally

Number of retrofit limestone WFGD 6 6 109 121 

Average sulfur content (%) of 2012 coal deliveries 0.55 0.43 1.82 1.63 

Total quantity (1,000 tons) of 2012 coal deliveries 12,375 11,637 148,631 172,644 

Potential process CO2 increase (%) from WFGD 0.3 0.2 0.9-1.0 0.8-0.9 

Note: Interconnection-level totals may not add up to national-level totals due to rounding; 

Potential process CO2 emissions are based on 85 and 95 percent removal rates, Western and 

Texas values round to the same quantity for 85 and 95 percent removal rates; 

Retrofits are for limestone WFGD only and do not account for other WFGD that do not produce 

process CO2 emissions 

2.6.4 Impact of future retrofit air pollution controls to comply with EPA and state 

environmental requirements 

This rule establishes CO2 intensity goals based on net electricity generation for each state using 

the state’s 2012 net electricity generation baseline. Many commenters noted that air pollution 

controls installed after 2012, many of which are required to comply with other federal and state 

environmental regulations, will require auxiliary power and therefore will increase EGUs’ net heat 

rate and CO2 intensity. These commenters suggested that EPA should account for the addition of 

future air pollution controls when calculating the state goals. EPA evaluated the potential impact 

of the additional pollution controls that are expected to be installed between 2013 and 2025 to 

comply with rules such as the Mercury and Air Toxics Standards (MATS), Clean Air Interstate 

Rule (CAIR), Cross-State Air Pollution Rule (CSAPR), Regional Haze Rule, as well as consent 

decrees and state policies. The agency’s analysis, described below, indicates that the potential 

impact on national net heat rate and, therefore, CO2 intensity is small – approximately 0.3 percent 

– in 2025. 
2 – 54 

To assess the anticipated impact of new retrofit air pollution controls on net heat rate, EPA used 

data and information from EPA’s Power Sector Modeling Platform v.5.15.80 The Base Case is a 

projection of power sector activity, taking into account existing federal and state air emission laws 

and regulations. The Base Case includes projected installations of air pollution controls and EGUlevel net electricity generation in the years 2017, 2018, and 2025. The agency compared the Base 

Case 2025 installations of air pollution controls to the 2012 installations listed in the National 

Electric Energy Data System (NEEDS) v.5.15. Specifically, the agency identified predicted 

additions of WFGD, dry flue gas desulfurization (DFGD), selective catalytic reduction (SCR), and 

selective non-catalytic reduction (SNCR) (see Table 2-40). 

Table 2-40 Predicted additional nameplate capacity (MW) 

with air pollution controls installed by 2025, by interconnection 

Air pollution control Western Texas Eastern

WFGD: wet flue gas desulfurization (MW) 283 0 18,214 

DFGD: dry flue gas desulfurization (MW) 505 584 15,388 

SCR: selective catalytic reduction (MW) 11,194 0 13,618 

SNCR: selective non-catalytic reduction (MW) 0 391 4,014 

To estimate the impact on heat rates, the agency used the heat rate penalties (i.e., auxiliary power 

requirements) from EPA’s Power Sector Modeling Platform v.5.13 (see Table 2-11). 81 Each 

EGU’s 2012 net heat rate was adjusted for the assumed heat rate penalties and multiplied by the 

EGU’s projected 2025 net electricity generation in the Base Case to estimate the incremental heat 

input required to operate the retrofit air pollution controls. The incremental heat input at EGUs 

with additional air pollution controls was aggregated to the interconnection region and then divided 

by the region’s pre-control heat input. 82 The anticipated impacts of the retrofit air pollution 

controls on net heat rate in 2025 are 0.31 percent in the Western Interconnection, 0.06 percent in 

the Texas Interconnection, and 0.31 percent in the Eastern Interconnection. 

 

80 More information about the Power Sector Modeling Platform, including data, assumptions, and documentation is 

available at http://www.epa.gov/airmarkets/programs/ipm/psmodel515.html. 

81 Information on air pollution controls is taken from Chapter 5 available at 

http://www.epa.gov/airmarkets/documents/ipm/Chapter_5.pdf. 

82 The pre-control heat input is calculated as 2012 net heat rate (before additional air pollution controls) multiplied by 

2025 net electricity generation. 
2 – 55 

Table 2-11 Assumed heat rate penalty for air pollution controls 

Air pollution control Heat rate penalty (%)

WFGD: wet flue gas desulfurization 1.70 

DFGD: dry flue gas desulfurization 1.33 

SCR: selective catalytic reduction 0.56 

SNCR: selective non-catalytic reduction 0.78 

This estimate is conservative for at least two reasons: (1) EPA’s cost performance assumptions 

tend to overestimate the auxiliary energy requirements from individual retrofit controls, and (2) at 

some EGUs these newer pollution control devices will replace existing pollution control devices. 

Accordingly, for these EGUs, the increase in net heat rate due to auxiliary power requirements to 

operate new controls will be at least partially offset by the decrease in net heat rate caused by 

removal of current control devices. The retrofit of air pollution control devices also present 

opportunities to improve net heat rate by replacing some equipment (e.g., fans, pumps, air heaters, 

economizers) with more efficient equipment.83 In addition, future technological innovations or 

advances in emission control practices may reduce the need for retrofit emission controls. 

Therefore, for the reasons described above, the agency has determined that when calculating 

overarching state goals, it is not necessary to account for the net heat rate impact of predicted 

installations of controls. However, EPA recognizes that states may choose to account for the 

installation of pollution controls at coal-fired EGUs after 2012 when developing their state plans. 

2.6.5 Impact of cycling operations and lower capacity factors on heat rate 

Several commenters expressed concern that the application of the other building blocks that make 

up the best system of emissions reductions will result in coal-fired EGUs operating at lower 

capacity factors and cycling more frequently during the interim and full compliance periods. 

Commenters noted that at lower hourly capacity factors, heat rate increases (e.g., see Figure 2-6) 

making improving heat rate even more challenging. EPA disagrees that the application of the other 

building blocks from the best system of emission reductions necessarily results in more cycling at 

existing coal-fired EGUs for the following reasons: (1) many coal-fired EGUs are expected to 

retire between 2012 and 2020, reducing the remaining capacity of existing coal-fired EGUs during 

the interim and full compliance periods; (2) EPA modeling of electricity generation in 2030 

 

83 For example, see D Osowski, 2014. “Bagging Two Birds with One Economizer,” Power Engineering. Available at 

http://www.power-eng.com/articles/print/volume-118/issue-11/departments/clearing-the-air/bagging-two-birdswith-one-economizer.html. 
2 – 56 

indicate that the remaining coal-fired EGUs will have higher annual capacity factors than the 884 

coal-fired EGUs in 2012 – the baseline for the agency’s heat rate improvement analyses; and (3) 

states have flexibility when developing their state plans and may choose policies that do not require 

heat rate improvements at specific EGUs. 

A significant amount of coal-fired EGU capacity has retired since 2012 and even more is expected 

to retire by 2020. For the 884 coal-fired EGUs in the agency’s analyses, more than 53,000 MW of 

capacity is expected to retire. This represents approximately 16 percent of the capacity from the 

884 coal-fired EGUs across the nation. In the Western Interconnection, coal-fired EGU owners 

and operators have officially announced 6,000 MW of capacity, 16 percent of total capacity, will 

retire in 2020 or earlier (see Figure 2-10). The agency does not have information about any official 

planned retirements in the Texas Interconnection. In the Eastern Interconnection, EGU owners and 

operators have officially announced more than 47,000 MW of capacity, 17 percent of total capacity, 

will retire in 2020 or earlier (see Figure 2-11). Because less coal-fired electricity generating 

capacity will be available for dispatch, the remaining coal-fired EGUs are expected to operate at 

higher annual capacity factors. 

Figure 2-10 Official completed and planned retirements of coal-fired EGUs in Western 

Interconnection, 2012–2020

Note: Capacity scale is 1/10 of scale in Eastern Interconnection (Figure 2-11) 
2 – 57 

Figure 2-11 Official completed and planned retirements of coal-fired EGUs in Eastern 

Interconnection, 2012–2020

EPA's power sector modeling for this rule predicts that annual capacity factors for coal-fired EGUs 

in 2030, with the rule in full effect, will be higher than the EGUs’ capacity factors in 2012. As 

noted above, the increased utilization of existing coal-fired EGUs that remain in operation is due 

in large part to significant retirements of coal-fired EGUs that have already occurred or are 

expected to occur as well as growth in electricity demand. Nationally, net annual capacity factors 

– net electricity generation divided by net summertime capacity – at coal-fired EGUs is predicted 

to be approximately 70 percent under a rate-based goal and 75 percent under a mass-based goal 

while the 2012 net annual capacity factor averaged 55 percent. 84 The values for the 

interconnections are shown in Table 2-52. While these values represent annual capacity factors 

and not hourly capacity factors, the substantial increase in annual capacity factors at existing coalfired EGUs in all interconnections except Texas leads to a reasonable conclusion that the remaining 

coal-fired EGUs as a whole will operate at higher levels in 2030 than the coal-fired EGU fleet did 

in 2012, the baseline for this heat rate improvement study. For the Texas interconnection, the 

capacity factors are anticipated to be approximately the same under a mass-based policy approach 

and lower under a rate-based approach. The state of Texas has the flexibility to consider this 

projected change in annual capacity factors when designing the state plan. 

 

84 The 2012 net annual capacity factors were calculated from the 2012 baseline data using the eGRID methodology. 

For the 947 coal steam EGUs in the CONUS, the capacity factor was calculated as net electricity generation 

(MWh)/[8784 hours X net summertime capacity (MW)]. The difference between the values in Table 2-12 and the 

values in Table 2-3 are attributable to the metric – Table 2-12 lists net capacity factor while Table 2-3 lists gross 

capacity factors. 
2 – 58 

Table 2-52 Annual net capacity factors (percent) in 2012 and 2030, by interconnection 

Interconnection 2012 2030 mass 

goal 

2030 rate 

goal 

Western 69.5 80.5 79.5 

Texas 66.5 66.5 55.8 

Eastern 52.8 74.9 69.5 

CONUS 55.3 74.7 69.5 

2.6.6 Influence of flow monitoring method on historical heat rate data 

To calculate heat input at a coal-fired EGU, the operator must measure the flow rate of flue gases 

as they pass through the stack (or duct). Initially, all coal-fired fired EGUs used EPA Reference 

Method 2 to measure stack gas flow.85 However, in 1999 EPA introduced new reference methods 

to address angular flow (Methods 2F and 2G) and the effect of the stack walls on gas flow (Method 

2H). In general, these alternative measurement methods reduce or eliminate the over estimation of 

stack gas flow that results from the use of method 2 when the specific flow conditions (e.g., angular 

flow) are present in the stack. Generally, the alternative methods lead to lower flow rates and as a 

result, lower heat input. After the introduction of these new methods, many coal-fired EGUs 

adopted the alternative methods to measure flow and calculate mass emissions.86 However, coalfired EGUs are not required to use the alternative measurement methods and they may change 

methods when conducting a RATA. Commenters have hypothesized that much of the variability 

in gross heat great is caused by coal-fired EGUs changing from one flow measurement method to 

another. EPA considered this possibility and discussed some of the issues in the proposed TSD. 

The agency evaluated the commenters’ concerns and conducted additional analyses to explore the 

potential impact of flow method on heat rate variability. 

.Evaluation of flow measurement methods is complex because coal-fired EGUs may have one or 

more stacks, including common stacks that are shared with other EGUs at the power plant, and 

might use different flow measurement methods for up to three different load levels in each stack. 

To assess the potential influence of flow measurement methods on heat rate variability, EPA 

reviewed the reported flow measurement methods for the 884 coal-fired EGUs between 2002 and 

2012. Of the 884 EGUs, 531 EGUs modified one or more flow measurement method for at least 

 

85 For more information about Method 2, see 40 CFR Part 60 Appendix A. 

86 Coal-fired EGUs use continuous emission monitoring systems to measure pollutant concentrations and stack gas 

flow. The two measured values are used to calculate mass emissions. 
2 – 59 

one load level during the 11-year study period (2002–2012). 87 If, as commenters suggested, 

changes to flow measurement methods were responsible for much of the variability in EPA’s data, 

the analytical results should show that EGUs with a change of flow measurement method have 

higher indicated potential heat rate improvements. To test this theory, the agency conducted the 

“efficiency and consistency” analysis using only the 531 EGUs that changed flow measurement 

methodology.88 However, as shown in 

Table 2-63, the EGUs that changed flow method during the study period (2002–2012) have lower

potential heat rate improvements in the Western and Texas interconnections than for the full group 

of coal-fired EGUs in the interconnections. In the Eastern interconnection, the results between the 

two groups – EGUs that changed flow measurement method(s) and all coal-fired EGUs – are 

nearly identical.89 This demonstrates that although changes in flow measurement method might 

have implications for an individual EGU’s reported heat rate, the changes do not adversely affect 

the overall interconnection-level results of EPA’s potential heat rate improvement analyses. 

Table 2-63 Potential heat rate improvement (percent) based on the 2-year efficiency and 

consistency under similar conditions analytical approach for all coal-fired EGUs and coalfired EGUs that changed flow measurement method, by interconnection 

Interconnection 884 coal-fired 

EGUs 

Coal-fired EGUs 

with flow 

measurement 

method changes 

Western 2.1 2.0 

Texas 2.3 0.0 

Eastern 4.3 4.3 

CONUS 4.0 3.8 

Because the potential heat rate improvement for the 884 coal-fired EGUs and the 531 coal-fired 

EGUs that changed one or more flow measurement methods are approximately the same in the 

 

87 The flow measurement methods for these 531 coal-fired EGUs are available in the dataset titled Supplemental data 

and analysis for potential heat rate improvement for the CPP final rule. 

88 EPA did not reassess the other analyses of the tripartite approach for EGUs that changed flow measurement 

methodology because the “efficiency and consistency” approach was the most conservative approach (i.e., resulted in 

the lowest potential heat rate improvement). 

89 This is particularly noteworthy given that the best system of emission reductions for the Eastern Interconnection 

sets the best system of emission reductions for the entire CONUS. 
2 – 60 

Western and Eastern Interconnection, EPA disagrees with commenters’ concern that changes to 

flow measurement methods were responsible for much of the variation in gross heat rates. In the 

Texas Interconnection, only six of 32 coal-fired EGUs reported changing flow measurement 

methodology during the study period (2002–2012). Collectively, the six EGUs in the Texas 

interconnection that changed flow measurement methods had performed better in 2012 and/or had 

lower variability than the population of Texas Interconnection coal-fired EGUs. 

In the year 2012, 193 of the 884 coal-fired EGUs used Method 2 for all load levels, 663 coal-fired 

EGUs used alternative flow measurement methods for all load levels, and 28 coal-fired EGUs used 

a combination of methods. 

For the reasons described above, changes in monitoring methodology at some coal-fired EGUs 

does not alter our confidence in the regional results that constitute the potential heat rate 

improvement under building block 1. 

2.6.7 Inability of individual coal-fired EGUs to achieve the heat rate improvements and 

concerns about stranded assets 

Several commenters stated that specific coal-fired EGUs are not able to achieve the heat rate 

improvements identified in the proposal TSD. They provide a variety of reasons, including: (1) the 

EGU’s fuel type (e.g., lignite has low energy value and high moisture content) makes it difficult 

to improve heat rate, (2) specific EGUs have already implemented efficiency measures and are not 

capable of improving heat rates beyond their current levels, and (3) small coal-fired EGUs do not 

have the same economies of scale as larger EGUs. Commenters also expressed concern that (1) 

soon-to-retire EGUs would be forced to make additional efficiency upgrades to improve heat rate, 

resulting in stranded assets, and (2) mandatory heat rate targets might force retirements, resulting 

in stranded assets.90

 

First, EPA disagrees with commenters that this rule establishes a mandatory “one-size-fits-all” 

heat rate improvement standard. Rather, the heat rate improvement is one part of the best system 

of emission reduction and must be viewed in the context of the full best system of emission 

reductions. As noted earlier, states have the flexibility to establish policies in the state plan that are 

reasonable and appropriate for that state; these policies may or may not include heat rate 

improvement goals. Second, EPA disagrees with the commenters’ presumption that each coalfired EGU must independently achieve the potential heat rate improvements identified in this 

analysis. The results of the analyses described above provide an estimate of potential heat rate 

improvements that, on average, are possible within each interconnection region based on each 

coal-fired EGU’s best historical heat rate between 2002 and 2012 and the EGUs’ 2012 heat rate. 

 

90 For more information about EPA’s analysis of the stranded assets question, see preamble section VIII.G.1, the legal 

memorandum, and the July 2015 docket memo titled “Stranded assets analysis.” 
2 – 61 

Because the results represent the average potential heat rate improvement for an interconnection, 

some coal-fired EGUs can make greater heat rate improvements while other EGUs might make 

less.91 Third, the agency’s analysis is based on coal-fired EGUs’ historical heat rates and is 

therefore demonstrated. Commenters also noted that the interim and full compliance periods do 

not begin for several years and new or enhanced technologies and practices for improving heat 

rate might be available in the future if there is a stronger incentive to improve efficiency. With 

regard to soon-to-retire EGUs, the interim compliance period does not begin until 2022 so the 

agency does not expect there would be significant requirements of those EGUs to make additional 

investments in heat rate improvements if a state chose to pursue heat rate improvements in its state 

plan. However, if a soon-to-retire EGU did make such investments, those investments might serve 

to extend the useful life of the EGU. 

2.6.8 Impact of equipment degradation 

Several commenters stated that EPA’s assessment of potential heat rate improvement from 

equipment upgrades was unrealistic because it did not account for degradation of the equipment 

over time. First, it is important to note that: (1) EPA is no longer basing potential heat rate 

improvements on specific upgrades and (2) the rule does not establish a federal requirement for 

coal-fired EGUs to meet specific heat rate improvement targets (see section 2.6.7). Second, the 

agency believes EGU operators can control the rate of degradation through proper maintenance 

and, when necessary, replacement or refurbishment of equipment. This is supported by information 

provided by a commenter referring to National Petroleum Council (NPC) and International Energy 

Agency (IEA) reports that discuss deterioration and enhanced maintenance practices at coal-fired 

EGUs. The NPC report stated: 

Deterioration naturally occurs, and if left unchecked it can become substantial. Therefore, some 

amount of deterioration, normal deterioration, will always be present and non-controllable. Most 

of the normal deterioration can be recovered with regularly scheduled maintenance intervals... 

There is a gradual increase in the unrecoverable portion as the unit ages which would require a 

replacement rather than a refurbishment to eliminate. Poor maintenance practices regarding the 

timing of the intervals and the amount of refurbishment may result in excessive deterioration and 

is controllable [by the EGU operator].92

 

 

91 The final CPP provides states the flexibility to develop emission standards for individual EGUs that more precisely 

take into account that EGU’s potential heat rate improvement – should states desire to consider potential heat rate 

improvement at all in establishing emission standards. Furthermore, if an affected EGU cannot meet the particular 

emission standard solely because it has below-average potential to reduce emissions through heat rate improvements, 

then in instances where the EGU’s state plan allows emissions trading, the EGU can acquire credits or allowances 

from affected EGUs that have above-average potential. 

92 See National Petroleum Council (NPC), 2007. Electricity Generation Efficiency. NPC Working Document; Topic 

Paper #4. Available at http://www.npc.org/Study_Topic_Papers/4-DTG-ElectricEfficiency.pdf. 
2 – 62 

The IEA report stated: 

[I]mprovements can not only restore ageing plants to their original performance, but also can 

often improve them further by avoiding the limitations of the prevailing technology at the time of 

original plant design...Not all of these areas have been considered worthy of attention previously, 

and a conclusion from that is that there may be other unrecognized areas of losses in efficiency 

which could be worth identifying.93

2.6.9 Limited adoption of low- and zero-cost heat rate improvements 

Commenters suggested that if there were low- or no-cost options to improve heat rate and, 

therefore, reduce fuel cost, coal-fired EGUs would have already implemented those measures 

because they have economic incentives to do so. However, research from NETL, RFF, and others 

show that there are a number of reasons that existing coal-fired EGUs might not implement lowand no-cost heat rate improvement practices, including: (1) concerns or uncertainties about New 

Source Review impacts; (2) lack of economic incentives to improve efficiency because of fuel 

adjustment clauses that enable electricity generating companies to “pass through” fuel costs 

directly to customers; (3) lack of management commitment for efficiency programs; (4) focus on 

optimizing profitability and ensuring availability; (5) lack of on-site performance engineers 

dedicated to heat rate improvement; (6) potential fuel cost savings represent only a small portion 

of total costs and are “of secondary concern when compared to needs for maintaining system 

reliability”; and (7) lack of regulatory certainty about future environmental regulations that lead to

operators deferring significant upgrades on older coal-fired EGUs.94

 

2.7 Costs 

2.7.1 Breakeven costs 

In the proposal TSD, EPA used an estimated average cost of $100/kW for a potential national 

average 6 percent heat rate improvement by coal-fired EGUs. Four percentage points of that 

potential improvement was projected to come from relatively inexpensive low- or no-cost best 

practices. Thus, most of the estimated $100/kW average cost actually represented the cost of 

relatively more expensive equipment and system upgrades that EPA expected would provide the 

remaining 2 percent improvement. 

 

93 See C Henderson, 2013. Upgrading and efficiency improvement in coal-fired power plants. International Energy 

Agency (IEA) Clean Coal Centre Report. CCC/221. ISBN 978-92-9029-541-9. Available at http://bookshop.ieacoal.org/reports/ccc-221/83186. 

94 See supra notes 11 and 18. Also see Comments of Natural Resources Defense Council (NDRC). Available at 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2013-0602-26818. 
2 – 63 

For this final rule, as explained above, EPA has refined the estimate of potential national average 

net heat rate improvement by coal-fired EGUs to between 2.1 to 4.3 percent for each 

interconnection, or about 4 percent nationally, with potentially all of that improvement coming 

from best practices. The agency no longer relies on an additional and distinct 2 percent 

improvement contribution from equipment upgrades. The agency expects, however, that some 

EGUs will in fact choose to make equipment upgrades for heat rate improvement, and that at least 

some small fraction of the overall heat rate improvement will come from equipment upgrades, and 

for at least a few EGUs most of the improvement may come from equipment upgrades. Overall, 

the agency expects the cost of heat rate improvement to be significantly less than $100/kW at the 

new level of heat rate improvement. However, because of the uncertainty associated with what 

fractions of a potential national average 4 percent heat rate improvement may come from no- or 

low-cost versus higher cost measures at individual EGUs, and considering commenter concerns 

that EPA’s previous estimated cost was too optimistic, the agency assessed the economic effects 

of a heat rate improvement cost that might range from $50 to $100/kW. 

The agency acknowledges here, as in the proposal TSD, that the technical applicability and 

efficacy of heat rate improvement measures, the cost of implementing them, and delivered coal 

cost, all depend upon site specific factors and can vary widely from site to site. Nonetheless, the 

agency finds it reasonable to expect that a fleet wide average cost for a national fleet wide average 

improvement of 4 percent, based primarily on low- and no-cost best practices, would be 

significantly less than $100/kW. However, as a conservative measure, and because the cost of heat 

rate improvement is one of the least costly and least impactful measures considered in the best 

system of emission reductions for this rule, the agency has decided to conservatively use the 

previously estimated $100/kW cost value for IPM modeling and the RIA estimates of the total cost 

of this final rule. 

The brief assessment presented here is intended only to illustrate the approximate effects of coal 

cost and heat rate improvement capital cost on the economic breakeven point between coal cost 

savings and the capital cost of implementing heat rate improvement. The resulting order-ofmagnitude cost range for CO2 removal ($ per ton) is also discussed. EPA does not suggest in the 

proposal nor in this final rule that heat rate improvement measures should meet a particular 

economic criterion (e.g., pay for themselves through reduced fuel costs) in order to be applied in 

state plans for compliance with this rule. 

Figure 2-12 presents a simplified economic breakeven analysis for a 4 percent nationwide average 

heat rate improvement, assuming there is no CO2 emission allowance cost involved. Additional 

assumptions used in the breakeven calculation include: capital charge rate of 14.3 percent, as used 

in the IPM Base Case modeling for this final rule; 2030 annual capacity factor of 78 percent, 

derived from IPM modeling; coal fleet generation-weighted average 2030 net heat rate of 10,250 

Btu/kWh before heat rate improvement, also derived from IPM modeling. 
2 – 64 

Figure 2-12 Breakeven economics of a 4 percent heat rate improvement 

Figure 2-12 shows that at a capital cost of $50/kW, the average fleet wide 2030 savings in coal 

cost would become greater than the annualized capital cost of an average 4 percent reduction in 

net heat rate when the average fleet wide coal cost exceeds about $2.50/mmBtu. For comparison, 

the average U.S. power sector delivered cost of coal was $2.38/mmBtu in 2012,95 and is projected 

to be about $2.70/mmBtu in 2030. 96 For different assumptions, the heat rate improvement 

economic breakeven point would change directionally as follows: 

If the heat rate improvement capital cost were on average less than $50/kW, 4 percent heat rate 

improvement would then “become economic” (i.e., pay for itself) at lower coal costs. For example, 

if the average capital cost were actually $25/kW, a fleet wide 4 percent heat rate improvement 

would become economic at an average coal cost of about $1.25/mmBtu, which is comparable to 

the U.S. power sector average costs of $2.38/mmBtu for all coal ranks and $2.89/mmBtu for 

bituminous coals in 2012.97 This sensitivity indicates that fuel cost savings alone would make it 

economic for some of those EGUs currently using the higher cost bituminous coals to make heat 

rate improvement investments. 

 

95 EIA, Electricity Data Table 7.4, Average Weighted Cost of Fossil Fuels for the Electric Power Industry 2002–2012. 

Available at http://www.eia.gov/electricity/annual/html/epa_07_04.html. 

96 2030 projected fuel costs are derived from IPM Base Case. 

97See supra note 953. 
2 – 65 

At an EGU net heat rate that is higher than the IPM projected 2030 average value of 10,250 

Btu/kWh, 4 percent heat rate improvement could be economic at coal costs lower than the values 

mentioned above. 

If there were additional heat rate improvement savings due to avoided future CO2 emission costs, 

heat rate improvement could become economic at lower coal costs, or at higher capital costs, or at 

lower heat rate reduction percentages. 

2.7.2 Estimated fleet wide CO2 reduction from and cost for heat rate improvements

EPA derived the following illustrative order-of-magnitude estimate of the fleet wide costeffectiveness of a 4 percent heat rate improvement from the IPM Base Case modeling: 

2.7.2.1 Fleet wide 2030 assumptions derived from IPM Base Case for this final rule 

• Coal fleet capacity applying combined heat rate improvement methods: 213,000 MW 

• Average net heat rate: 10,250 Btu/kWh net, before 4 percent heat rate improvement 

• Average capacity factor: 78 percent 

• Heat rate improvement Btu and CO2 reduction: 4 percent 

• Heat rate improvement capital cost: $50/kW (assumed) 

• Annual capital charge rate: 14.3 percent 

• Average coal cost: $2.70/mmBtu 

2.7.2.2 Estimated fleet wide results 

• Fleet wide CO2 reduction via heat rate improvement: 62 million ton/year 

• Total heat rate improvement capital cost: $10.7 billion 

• Annualized heat rate improvement capital cost: $1.5 billion 

• Annual coal cost savings: $1.6 billion 

• Annual net savings: $0.09 billion 

• Annual net savings for CO2 reduction: $1.4/ton 

2.7.3 Conclusion - heat rate improvement economics 

This necessarily simplified analysis supports the following summary conclusions: 

1. Some degree of heat rate improvement is very likely already economic (paying for 

itself) for high heat rate-high coal cost EGUs, and will remain so in 2030 

2. If a fleet wide average 4 percent heat rate improvement were implemented, it would 

also pay for itself on the basis of fuel savings alone, before consideration of the value 
2 – 66 

of the associated CO2 emission reductions, on a fleet wide basis at projected 2030 coal 

prices if the associated average capital cost is about $50/kW or less. 

3. If a fleet wide average 4 percent heat rate improvement were implemented, and the 

associated average capital cost is as much as $100/kW, 4 percent heat rate 

improvement would not likely pay for itself based on fuel cost savings alone. 

4. Even at a capital cost of $100/kW and an IPM projected 2030 coal price of 

$2.70/mmBtu, the average fleet wide cost of CO2 reduction via 4 percent heat rate 

improvement would be a still relatively low $23/ton. 

Thus, although there is currently some uncertainty associated with the costs of achieving a 

particular fleet wide amount of heat rate improvement, it is clear that heat rate improvement is an 

available low-cost approach to CO2 reduction for existing coal-fired EGUs. 
3 – 1 

CHAPTER 3: CO2 REDUCTION POTENTIAL FROM GENERATION 

SHIFTS AMONG EXISTING UNITS 

3.0 Overview 

This chapter explores the dynamics of power sector dispatch and the cost-effectiveness of lowering 

the carbon dioxide (CO2) emissions intensity of the power sector by substituting generation from 

the most carbon-intensive existing EGUs and increasing utilization, to the extent possible, of less 

carbon-intensive existing fossil fuel-fired EGUs. More specifically, the examination focuses on 

opportunities to improve emissions intensity by increasing the utilization of existing natural gas 

combined cycle units. The TSD provides background on existing power plants, power system 

operation, and the economics of electricity production and delivery in the context of cost-effective 

CO2 emission reduction opportunities. 

3.1 Introduction 

Electric system dispatch is typically defined as “the operation of generation facilities to produce 

energy at the lowest cost to reliably serve consumers, recognizing any operational limits of 

generation and transmission facilities.”1 Electricity demand varies across geography and time in 

response to numerous conditions, such that electricity generators are constantly responding to 

changes to meet demand in the most reliable and cost-effective manner possible. 

The nation’s EGUs are connected by transmission grids that extend over large regions. Through 

these interconnections, EGU balancing authorities treat the product (i.e., electricity) of EGUs as 

fungible, calling for electricity generation supply to meet demand usually by deploying the least 

expensive power source first, subject to the constraints of reliable system operation.2 

EGU operators and balancing authorities must take into account several constraints in dispatch, 

including transmission and other operating constraints as well as emission control programs and 

other environmental requirements. Such programs and requirements can change the relative cost 

of generating electricity among plants and/or limit the number of hours that a plant can run. For 

many years, EGU operators throughout the country have considered the emissions implications 

for pollutants such as SO2 and NOx when scheduling unit dispatch, in response to costs and 

regulatory requirements. For example, EGU operators in 10 states participating in the Regional 

Greenhouse Gas Initiative have several years of experience with factoring CO2 emissions limits 

directly into bids for economic dispatch. The electric system’s carbon intensity can be lowered by 

 

1

 Energy Policy Act of 2005 

2

 A balancing authority is the responsible entity that integrates resource plans ahead of time, maintains the balance 

between supply, demand, and generation within a balancing authority area, and supports interconnection frequency 

in real-time. http://www.nerc.com/files/glossary_of_terms.pdf 
3 – 2 

shifting generation among existing EGUs, particularly by shifting generation from coal-fired units 

to natural gas combined cycle (NGCC) units. 

3.2 Power Sector Background 

3.2.1 Electric Dispatch 

The operation of electricity generation accords to the principle of security-constrained economic 

dispatch, which is “the operation of generation facilities to produce energy at the lowest cost to 

reliably serve consumers, recognizing any operational limits of generation and transmission 

facilities.”3

 The cost of operating electric generators varies based on a number of factors, such as 

fuel used and generator efficiency. In many regions of the country, System Operators help 

coordinate economic dispatch over large areas to help keep the cost of meeting electricity demand 

as low as possible, subject to operational constraints. 

The decision by balancing authorities to call upon, or dispatch, any particular generating unit is 

generally driven by the relative operating cost, or marginal cost, of generating electricity to meet 

the last increment of electricity demand. These costs change over time depending upon a variety 

of factors like fuel prices, weather conditions, and overall demand levels. Since the fixed cost of 

power plants is a sunk cost, plant operators usually bid into electricity markets such that their 

variable costs are covered. For fossil fuel-fired electric generating units, variable costs are 

dominated by the cost of the fuel, although coal-fired units often also have considerable variable 

costs associated with running pollution controls.4 Other generating technologies, like renewables, 

hydroelectric, and nuclear, have little or no variable costs and are generally dispatched to the extent 

possible. Under economic dispatch, the units with the lowest variable costs will be called upon 

first, then other units (with higher variable costs) will be called upon sequentially, such that total

system demand is met. The merit order in which units are dispatched to meet demand, at any 

particular point in time, is commonly called a dispatch or supply “curve.”5

 

3.2.2 Balancing Authorities 

In states with cost-of-service regulation of vertically-integrated utilities who own generation, 

transmission, and distribution infrastructure, the utilities themselves are often also the balancing 

authorities who determine unit dispatch. Such utilities are presumed to dispatch their units in a 

cost-minimizing fashion (seeking the lowest cost), and they can arrange to buy and sell power with 

other balancing authorities. 

 

3

 Federal Energy Regulatory Commission, 2005. Economic Dispatch: Concepts, Practices, and Issues 

4

 In addition to fuel costs, variable costs also include costs associated with operation and maintenance, and costs of 

operating a pollution control and/or the price of allowances to emit certain pollutants. 

5

 http://www.eia.gov/todayinenergy/detail.cfm?id=7590 
3 – 3 

In states that have restructured regulation to allow for competition between generators, RTOs and 

ISOs are generally responsible for moving electricity across larger areas in the most efficient (leastcost) manner possible.6

 RTOs and ISOs coordinate, control, and monitor the operation of the 

electric power system to ensure cost-effective and reliable delivery of power, and they are 

independent from market participants. ISOs grew out of the Federal Energy Regulatory 

Commission (FERC) requirements for existing power pools to satisfy the requirement of providing 

non-discriminatory access to transmission. Subsequently, FERC encouraged the voluntary 

formation of RTOs to administer the transmission grid on a regional basis throughout North 

America (including Canada). 

RTOs and ISOs administer wholesale power markets, which match the generation of electricity 

with the purchase of electricity (and ancillary services) prior to delivery to end-users. Companies 

that provide retail electricity (e.g., utilities and energy service companies) procure power through 

these wholesale electricity markets. 

3.2.3 State Public Utility Commissions (PUCs)7

Each state has a governing body that is tasked with regulating retail electricity rates and electric 

services to protect the public interest, ensure efficient and reliable delivery of electricity, and plan 

appropriately for the short and long term energy needs of the state and consumers. Depending on 

market structure, PUCs also allocate costs among customers, design price structures and set price 

levels, set service quality standards, approve capital expenditure by utilities, and arbitrate disputes 

among relevant parties and stakeholders. In restructured markets, the PUC’s authority is generally 

applicable to the transmission and distribution system, since the revenues from generation (and 

sometimes capacity) are earned through markets governed by RTOs and ISOs. In cost-of-service 

states, the PUC also has oversight of the generation and capacity planning components. 

3.2.4 Wholesale Power Markets 

RTOs and ISOs operate spot markets for wholesale power supply and demand for their designated 

area, including both day-ahead and real-time markets (using hourly or shorter time periods). These 

markets are based on bids for supply and demand and operate according to rules established by 

FERC. The RTOs and ISOs use these markets for balancing power supply and load in their area, 

serve as the reliability coordinator for their area and typically serve as the balancing authority for

the same area. 

For areas not administered by RTOs and ISOs, dispatch is scheduled on both a day-ahead and an 

hourly basis, but it is typically driven by the power supply costs and schedules of traditional 

utilities. This dispatch will depend to a certain degree on spot markets for power, since utilities 

 

6

 http://www.ferc.gov/industries/electric/indus-act/rto.asp 

7

 These entities are sometimes called Utilities Commissions, Utility Regulatory Commissions, or Public Service 

Commissions. 
3 – 4 

will dispatch purchased power from other suppliers when that power can be obtained at a cost 

savings. There is an active wholesale market for this power in the spot market, from individual 

sales and from exchanges. These markets typically sell power on a day-ahead basis rather than on 

an hourly basis, and they can also sell power for longer periods of time, such as weekly or monthly. 

However, the actual dispatch and balancing of power is conducted by the utility based on its own 

scheduling, and purchasing protocols and varies considerably from one utility to the next. 

3.2.5 Reliability 

As a balancing authority, the ISO/RTO or utility will balance demand, generation, and 

imports/exports in real time while ensuring that the next hour’s demand, or load, is met. As this 

happens, the electric power system is constantly monitored load is dispatched to ensure reliability 

limits are met, voltage levels are appropriate, system frequency is maintained and appropriate 

corrective action is taken when needed. 

A reliability coordinator is responsible for the generation-demand balance within its reliability 

coordination area of the bulk power system, and may direct a balancing authority within its 

reliability coordinator area to take whatever action is necessary to ensure that this balance does not

adversely impact reliability. The bulk power system refers to a large interconnected electrical 

system made up of generation and transmission facilities and their control systems. To ensure 

reliability, system operators continuously analyze real-time and forecasted load and transmission 

conditions to ensure that scheduled generation dispatch can meet load without adverse impacts. If 

the scheduled dispatch is not feasible within the limits of the transmission system, it must be 

adjusted by the system operator. The North American Electric Reliability Corporation (NERC) 

develops and enforces the procedures to ensure reliability, in accordance with Federal laws and 

regulations, and with FERC oversight.8

 

3.3 Generation Shifting: Technical Potential 

In 2012, national average CO2 emission rates9 across the following technology types on a net 

generation basis were: 

• Coal Steam: 2,217 lbs/MWh 

• Oil/Gas (O/G) Steam: 1,435 lbs/MWh 

• NGCC: 905 lbs/MWh 

 

8

 http://www.nerc.com/ 

9

 Emission rates in this document are shown on a net generation basis and reflect Hawaii and Alaska sources, as well 

as units that commenced construction in 2012. See “2012 unit-level data using the eGrid methodology” file provided 

in the docket 
3 – 5 

Coal- and oil/gas-fired boilers are considerably higher-emitting sources than NGCCs, on average. 

Therefore, shifting generation from the average existing fossil fuel-fired boiler to an average 

existing NGCC will result in notable CO2 emission reductions. 

The lower emission rate of NGCC conveys the potential of generation shifting to reduce GHG 

emissions. However, the actual potential to realize emission reductions through this technology 

depends on the availability and capacity factors of the existing NGCC fleet. In order to shift 

generation from existing fossil fuel-fired boilers to existing NGCC, there needs to be some existing 

unused generation potential in the current NGCC fleet that could replace generation from more 

CO2-intensive generating resources. 

Given their cost structure, for purposes of economic dispatch, most NGCCs have historically 

operated as base load or intermediate load10 due to their high efficiency and flexibility of operation, 

with national average annual capacity factors in the range of 40-50%.11,12 However, NGCCs are 

designed for, and are demonstrably capable of, reliable and efficient operation at much higher 

annual capacity factors, as shown in observed historical data for particular units and their design 

and engineering specifications. 

The capability of NGCCs to operate is indicated, in part, by data on the average availability factor 

of NGCCs. 13 The term “availability” is a common engineering term used in the power sector that 

reflects the percentage of period hours that a plant is available to produce electricity (a period 

being 1 year, or 8,784 hours in 2012 since that year included a leap day). The unavailable period 

is generally attributable to scheduled maintenance, unplanned maintenance, and unplanned 

outages. For modeling purposes, EPA assumes that NGCC has an availability of 87%.14 Other 

reports suggest that NGCC availability factors may reach as high as 92%.15,16 Many modern 

NGCCs have demonstrated availability factors of over 95%. According to one NGCC 

manufacturer, these highly efficient units already represent over 15 percent of total installed 

capacity nationwide, including all electric generating sources (as of 2010).17 These high-efficiency 

and high-availability NGCC units were first introduced around 1995 and have consistently 

 

10 Unlike baseload power, intermediate load is cycled to follow the changing demand for electricity, but it is not 

cycled as often as peaking supply which operates on much shorter timescales. 

11 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayinenergy/detail.cfm?id=14611 (for recent data) 

12 EIA, Electric Power Annual 2009, http://www.eia.gov/electricity/annual/archive/03482009.pdf (Table 5-2 for 

2009 and earlier data) 

13 NERC, 2008-2012 Generating Unit Statistical Brochure – All Units Reporting, 

http://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx

14 See Chapter 3, Table 3-18 at http://www.epa.gov/powersectormodeling/BaseCasev513.html 

15 http://www.power-eng.com/articles/print/volume-115/issue-2/features/higher-availability-of-gas-turbinecombined-cycle.html 

16 Power Engineering, Negotiating Availability Guarantees for Gas Turbine Plants, 03/01/2001, http://www.powereng.com/articles/print/volume-105/issue-3/features/negotiating-availability-guarantees-for-gas-turbine-plants.html

17 http://site.ge-energy.com/corporate/network/downloads/7FA_Evolution.pdf 
3 – 6 

reported availability factors of 90 to 92 percent to NERC.18 These availability factors are 

substantially above the 2012 NGCC generation level, indicating that there is a significant potential 

for increasing generation from unutilized existing NGCC capacity. 

To evaluate potential opportunities for shifting generation to unused NGCC capacity, EPA 

reviewed recent operating data across all NGCCs to determine NGCC capacity factors in 2012. 

For GHG abatement purposes, generation shifting would require one net MWh of generation from 

a lower-emitting technology operating in place of one net MWh of generation from a higheremitting technology. Therefore, EPA calculated capacity factors as the ratio of a unit’s reported 

net generation to its potential net generation, the latter of which is determined by the unit’s net 

generating capacity. Net generating capacity reflects a reduction from nameplate capacity due to 

on-site electricity use (e.g., station service or auxiliaries) and local temperature conditions. EPA 

used the net summer capacity reported for units in the EIA Form 860 because it controls for local 

conditions and is thus a consistent metric for evaluating dependable capacity nationwide. Potential 

net generation was calculated as the net summer generation capacity of a unit multiplied by the 

number of hours in a year. Therefore, the equation for calculating capacity factors is: 

Reported generation in 2012

Net summer capacityx (hours in 2012)

This calculation of capacity factor provides an indication of how much net generation a unit is 

providing as a percent of its total net generating capacity. The capacity factor refers to the actual 

utilization of that source on an annual basis, while that source’s availability refers to its potential 

maximum utilization level. EPA surveyed 2012 data for 439 NGCC plants and observed that the 

NGCC fleet had an average capacity factor of 46% in 2012.19 Since the fleet-wide capacity factor 

in 2012 was less than the technical generation potential available for the technology, the historical 

data suggests that there is a significant potential for shifting generation from higher CO2 emitting 

resources to lower CO2 emitting NGCC generation. 

Availability for NGCC fleet................................................. 87% to 92% 

2012 Capacity Factor for NGCC fleet ............................................. 46% 

 

18 GE Power Systems submitted to U.S. Department of Energy, 2000. Utility Advanced Turbine Systems 

Technology Readiness Testing Phase 3 Restructured. DOE Cooperative Agreement No. DE-FC21-95MC31176—

30. 

19 EIA 860 and 923. 
3 – 7 

To quantify the GHG reduction potential from generation shifting, EPA considered alternative 

capacity factor levels at which the NGCC fleet could be dispatched. Although the availability for 

NGCC units is assumed to be 87% to 92%, EPA did not assume that the NGCC fleet could 

collectively operate at this level on an annual basis. To determine a reasonable average capacity 

factor for the NGCC fleet as part of BSER, EPA considered historical data and modeling 

projections describing NGCC characteristics and operating behavior. 

As seen in Table 3-1, the existing NGCC fleet is relatively young. More than 80% of the capacity 

has come online in the last 15 years.20 Of this capacity, almost all are highly efficient NGCCs that 

are able to achieve high availability factors. 

Table 3-1: Existing NGCC Capacity, by Age21

Online Year 

Capacity 

(Name Plate Capacity – MW) 

Percentage of 

Total Existing NGCC 

Fleet, by Capacity 

Pre 1950 103 0% 

1950-1959 1,769 0.7% 

1960-1969 3,087 1.3% 

1970-1979 6,909 2.8% 

1980-1989 7,658 3.1% 

1990-1999 28,467 11.7% 

2000-2009 174,947 71.7% 

2010+ 21,068 8.6% 

Total 244,008 100%

 

20 See the National Electricity Energy Data Systems (NEEDS) file at 

http://www.epa.gov/powersectormodeling/BaseCasev513.html 

21 See “Operable” worksheet in “GeneratorY2012” Workbook in 2012 Zip file at 

http://www.eia.gov/electricity/data/eia860/ 
3 – 8 

Of 439 NGCC plants generating in 2012, EPA observed that 67 plants (more than 15% of NGCC 

plants) had a capacity factor of 75% or greater (see Figures 3-1 and 3-2).22 

Figure 3-1: NGCC Plant Distribution by Capacity Factors (2012) 

Source: EIA forms 860, 923 (2012), Combined Cycle Steam Part (CA) and Combined Cycle 

Combustion Turbine Part (CT) prime mover categories

 

 

22 See “2012 unit-level data using the eGrid methodology” file provided in the docket 

0

5

10

15

20

25

30

35

40

Number of NGCC Plants

2012 Capacity Factor
3 – 9 

Table 3-2: Plant Distribution of Existing NGCCs (2012) 

Capacity Factor 

(net summer) 

# of NGCC 

plants 

% of NGCC 

Plants 

Less than 5% 32 7.3% 

5%-9% 23 5.2% 

10%-14% 17 3.9% 

15%-19% 23 5.2% 

20%-24% 22 5.0% 

25%-29% 13 3.0% 

30%-34% 21 4.8% 

35%-39% 23 5.2% 

40%-44% 26 5.9% 

45%-49% 25 5.7% 

50%-54% 29 6.6% 

55%-59% 30 6.8% 

60%-64% 27 6.2% 

65%-69% 38 8.7% 

70%-74% 23 5.2% 

75%-79% 30 6.8% 

80-84% 19 4.3% 

85-89% 7 1.6% 

Greater than 90% 11 2.5% 
3 – 10 

In 2012, more than 15% of NGCC plants operated at an annual capacity factor of 75% or higher 

on a net summer basis. This subset of NGCCs was largely dispatched to provide base load power. 

While only 15% of plants operated at 75% or higher capacity factor on an annual basis, the fleet 

of NGCC units was relied upon heavily during certain periods of time, in response to higher 

demand. On a seasonal basis, a significant number of units achieved capacity factors greater than 

50%, and even up to 80%. During periods where demand levels are typically lower, some NGCCs 

were idled or operated at lower capacity factors. Nonetheless, a notable number of existing NGCCs 

have demonstrated the ability to achieve a 75% or higher capacity factor for extended periods of 

time. These units achieved high capacity factors without adverse effects on the electric system. 

Hourly data reported to EPA23, particularly from the peak summer months of July and August, also 

show that most individual NGCC units can operate at high capacity factors. Capacity factors from 

the hourly unit level data reported to EPA are based on heat input and input capacity; these 

calculations correspond closely to the capacity factor calculations described above (that are based 

on data reported to EIA representing longer periods of time) and enable consideration of capacity 

factors at the unit and hourly level. Using EIA and EPA data from 2012 for the same facilities, 

capacity factor averages from EIA nameplate values and EPA unit level data from 2012 data are 

both 46%, while net summer capacity factors from EIA are 52%.24 On average, then, net capacity 

factors are approximately 5% or more above nameplate capacity factors. Analysis of these hourly 

data demonstrates several important point about the feasibility of running NGCC units at capacity 

factors above 70% nameplate – or 75% net summer – levels. To examine historical potential 

capacity factors for NGCC units, EPA calculated nameplate capacity factors for daily peak 

operation from 7 AM to 7 PM during the months of July and August, a sustained period that 

demonstrates both the ability of individual units to operate at high capacity factors and of the power

grid to support regional operation of the NGCC fleet at much higher levels than current averages. 

This analysis shows the following key points: 

• Most individual units are capable of high levels of operation: 88% of NGCC units 

operated at a nameplate capacity factor of 70% or above for at least one day in July and 

August of 2012; 67% operated at 80% or above for at least one day. 

• The interconnected power grids can support region-wide NGCC operation at much 

higher than average capacity factors. Each NERC region operated at a nameplate capacity 

70% or above for at least one day, as shown below: 

o ERCOT 75% 

o WECC 74% 

o Eastern Interconnection Regions 

 RFC 75% 

 MRO 76% 

 

23 Air Markets Program Data (at http://ampd.epa.gov/ampd/). 

24 EPA Air Markets data is unit level data from 411 facilities; EIA data surveys are from 439 facilities. 
3 – 11 

 SERC 73% 

 FRCC 75% 

 SPP 71% 

 NPCC 70% 

These observations demonstrate the technical ability to operate existing NGCC capacity at or 

above a 70% nameplate capacity factor, and therefore at least at a 75% net summer capacity factor 

or higher. These observations also demonstrate the ability of the natural gas transmission system 

to support this level of generation. 

3.4 Historical Trend 

Over the last several years, advances in the production of natural gas have helped reduce natural 

gas prices and improved the competitive position of gas-fired units relative to coal-fired units. As 

a result, operators have shifted significant quantities of generation from coal units to NGCCs, 

absent any federal CO2 requirements. 2012 net generation from NGCC units grew to 966 TWh, up 

from 783 TWh in 2011 (23% growth in one year).25

 

An analysis of historical generation across the fleet of coal and NGCC units for 2011 and 2012 

provides some implicit measures of the cost dynamics between the two technologies. For example, 

one is able to look at the change in the prices of coal and gas to gauge the relative costs of 

generating for each technology. While there are wide-ranging costs at the unit level, an aggregated 

assessment of the relative economics is informative and can provide a metric for assessing the 

implications of this generation pattern as it relates to emissions of CO2. 

The potential for shifting generation from CO2 intensive sources to less CO2 intensive sources is 

evidenced in historical data. EIA form 860 and 923 data demonstrate an increase in NGCC 

generation and fuel use between 2011 and 2012 of more than 20% (even though the NGCC fleet 

capacity rose by just 3%). As NGCC generation rose by approximately 183 TWh, coal generation 

fell by approximately 217 TWh. The significant shift from coal to gas generation over just a one 

year period demonstrates the ability for a quick shift in generation patterns in response to market 

or economic drivers. 

The increase in NGCC utilization was in large part driven by a decrease in natural gas prices (see 

Table 3-3). Electric power sector delivered natural gas prices averaged $4.89/Mcf in 2011 and 

$3.54/Mcf in 2012.26 This price decline of $1.35/TCF created an additional incentive for 

decreasing coal generation and increasing NGCC generation relative to 2011 electricity production 

economics. This historical data also shows a sharp increase in the NGCC fleet’s net summer 

 

25 EIA Form 860, 923 (2011) 

26 EIA Natural Gas Prices, http://www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_a.htm 
3 – 12 

capacity factor from approximately 40% on average in 2011 to approximately 46% on average in 

2012. 

The historical trend of shifting generation to NGCC is expected to continue, as dry natural gas 

production is expected to grow as much as 23% by 2022 relative to its 2012 level.27

Table 3-3: 2011 and 2012 Gas and Coal Generation28

Year 

NGCC Net 

Summer 

Capacity 

(GW) 

NGCC Net 

Generation 

(TWh) 

NGCC 

Capacity 

Factor 

Electric 

Power 

Sector 

Natural Gas 

Price 

($/TCF) 

Coal Net 

Generation 

(TWh) 

2011 204 783 40% $4.89 1,718 

2012 211 966 46% $3.54 1,501 

 

27 AEO 2015 

28 EIA form 860 and EIA form 923 (NGCC), and EIA Monthly Energy Review, Table 7.2b (coal) 
3 – 13 

Table 3-4. Electric Power Sector Electricity Net Generation from Natural Gas, 1990-2012 

Year 

Net Generation: Natural Gas 

(million KWh) 

Percent Increase 

from Previous 

Year 

1990 309,486 

1991 317,773 3% 

1992 334,274 5% 

1993 342,222 2% 

1994 385,689 13% 

1995 419,179 9% 

1996 378,757 -10% 

1997 399,596 6% 

1998 449,293 12% 

1999 472,996 5% 

2000 517,978 10% 

2001 554,940 7% 

2002 607,683 10% 

2003 567,303 -7% 

2004 627,172 11% 

2005 683,829 9% 

2006 734,417 7% 

2007 814,752 11% 

2008 802,372 -2% 

2009 841,006 5% 

2010 901,389 7% 

2011 926,290 3% 

2012 1,132,791 22% 

Source: EIA Monthly Energy Review, Table 7.2b (multiple years) 
3 – 14 

3.5 Phase-In 

The application of an increase in NGCC operation equivalent to a 75% net summer utilization to 

the baseline data from 2012 would yield an increase of about 55% in NGCC generation, from 966 

TWh to 1,498 TWh. To allow time for potential infrastructure improvements to facilitate increased 

utilization of existing NGCC plants, EPA is finalizing a gradual phase over the interim period, 

consistent with the historical data presented above. The phase-in schedule is based on two 

parameters. The first parameter defines an amount of generation shift to existing NGCC capacity 

that is feasible by 2022. This parameter is conservatively defined as a percent increase from 2012 

to 2022 that is equal to the largest single largest annual increase in power sector gas-fired 

generation since 1990. It is reasonable to assume that the same increase in NGCC utilization that 

occurred in one year could be implemented again by 2022. 

The increase in NGCC utilization over the remaining years of the interim period is limited by the 

average annual growth in gas-fired generation in the power sector between 1990 and 2012, or 

approximately 5 percent per year. Since this is the average annual increase in gas utilization that 

has occurred over recent history, it is reasonable to assume that this increase could occur 

throughout the interim period, up to the full technical potential of 75% utilization on a net summer 

basis. 

In the performance rate calculation methodology, these two parameters constrain the annual rate 

at which building block 2 shifts generation from fossil steam units to NGCC units. As discussed 

in the preamble, the interim performance rate is an average of annual rates calculated over the 

2022-2029 period. The two parameters above limit the extent to which NGCC generation is 

assumed to increase and replace fossil steam generation in each year of the interim period. From 

historical data, we can observe that the single largest annual increase in power sector gas-fired 

generation since 1990 occurred between 2011 and 2012 and is equal to 22 percent. Therefore, in 

the first year of the interim period, NGCC generation is limited to a maximum of a 22 percent 

increase from 2012 levels in each region. In each subsequent year, regional NGCC generation is 

limited to a maximum of a 5 percent increase from the previous year. This phase-in continues in 

the performance rate-setting methodology until the BSER level of shifting from fossil steam 

generation to NGCC generation is completed. Under this approach, building block 2 is completely 

phased into the performance rates of all regions by the end of the interim period. Table 3-5 

summarizes the quantity of NGCC generation that represents building block 2 potential as 

incorporated in the emission performance rate calculation in each region and year. 
3 – 15 

Table 3-5. BSER Maximum NGCC Generation by Region and Year (TWh). 

NGCC Generation (TWh) 

Region 

Maximu

m 

Potential 

at 75% 

2012 

(adjusted)

BSER Maximum 

202

2 

202

3 

202

4 

202

5 

202

6 

202

7 

202

8 

202

9 2030

Limit 

-- -- 22% 5% 5% 5% 5% 5% 5% 5% 5% 

Eastern 

Interconnection 

988 735 896 941 988 988 988 988 988 988 988 

Western 

Interconnection 

306 198 242 254 267 280 294 306 306 306 306 

Texas 

Interconnection 

204 137 167 176 185 194 203 204 204 204 204 

Source: Preamble, Section V.D. 

3.6 Natural Gas Supply 

 EPA expects that the gradual increase in generation from existing NGCC as defined above to be 

feasible and consistent with domestic natural gas supplies in each year of the 2022-2029 phase-in. 

Increases in the natural gas resource base have led to fundamental changes in the outlook for 

natural gas. There is general agreement that recoverable natural gas resources will be substantially 

higher for the foreseeable future than previously anticipated, exerting downward pressure on 

natural gas prices.29

According to EIA, natural gas proved reserves have doubled between 2000 and 2012.30 Domestic 

dry gas production has increased by 25% over that same timeframe (from 19.2 TCF to 24.0 TCF). 

EIA’s Annual Energy Outlook for 2015 projects that production will further increase to 29.5 TCF 

by 2022, due to increased supplies and favorable market conditions. For comparison, building 

 

29 National Petroleum Council. 2011. Prudent Development: Realizing the Potential of North America's Abundant 

Natural Gas and Oil Resources. http://www.npc.org/reports/rd.html (see Figure 1.2 on p. 47). 

30 http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=3&pid=3&aid=6 
3 – 16 

block 2 assumes NGCC generation growth of 340 TWh from 2012 to reach the level assumed for 

2022, and that NGCC generation growth would result in increased gas consumption of less than 2 

TCF for the electricity sector, which is less than EIA’s projected increase in natural gas production 

of 5.5 TCF from 2012 to 2022.31

 

The National Petroleum Council (NPC), a privately funded advisory committee established by the 

Secretary of Energy, recently updated a major resource study and concluded that “the potential 

supply of North American natural gas is far bigger than was thought even a few years ago,” after

large increases in shale resource estimates had already been realized.32 

Figure 3-2: U.S. Natural Gas Technically Recoverable Resources (from NPC, 2011)33

 

31 Assuming 1,028 Btu/cubic foot and 7,615 Btu/KWh 

32 National Petroleum Council, 2012 (http://www.npc.org/PD_update-80112.pdf) 

33 http://www.npc.org/reports/rd.html 
3 – 17 

3.6.1 Natural Gas Deliverability 

To examine the potential for increases in pipeline deliverability, EPA analyzed the pipeline flow 

data from the Energy Information Administration. These data provide pipeline capacity for inflows 

and outflows by region (see Figure 3-3). However, we cannot measure natural gas deliverability 

solely by considering a region’s inflow capacity, since that region may need to allow some of that 

inflowing natural gas to become outflow to other regions (effectively passing that natural gas 

through the region to consumers in other regions). Consequently, net capacity – the difference 

between inflow capacity and outflow capacity -- is a better metric for a region’s ability to increase 

deliverability of natural gas supply, because natural gas using such pipeline net capacity must be 

consumed within the region (as there would be no more outflow capacity for it to leave the region). 

The regions used by EIA for measuring regional natural gas deliverability are shown in Figure 3-

3. Our assessment focuses on areas that would experience increases in natural gas consumption in 

line with building block 2’s quantified potential for increased utilization of NGCC; in other words, 

these areas have most of the unutilized NGCC capacity. These are the Northeast, Southeast, 

Midwest and Western regions in Figure 3-3. The net pipeline capacity for these regions from 2005 

to 2011 is shown in Table 3-6 below. 

Table 3-6. Natural Gas Pipeline Net Capacity by Region, 

2005 - 2011 by Gas Consuming Area34

Region 2005 2006 2007 2008 2009 2010 2011

Capacity in MMCF/day 

Midwest 17,102 17,232 17,452 17,302 18,714 18,564 18,414 

Northeast 11,199 11,219 11,384 11,929 12,079 12,229 12,379 

Southeast 12,921 12,901 12,736 15,741 18,241 20,797 20,797 

Western 11,882 11,882 12,496 12,496 12,496 12,641 14,407 

All Areas 53,104 53,234 54,068 57,468 61,530 64,231 65,997 

Percent Change from 2005 

Midwest 0.0% 0.8% 2.0% 1.2% 9.4% 8.5% 7.7%

Northeast 0.0% 0.2% 1.7% 6.5% 7.9% 9.2% 10.5%

Southeast 0.0% -0.2% -1.4% 21.8% 41.2% 61.0% 61.0%

Western 0.0% 0.0% 5.2% 5.2% 5.2% 6.4% 21.3%

All Areas 0.0% 0.2% 1.8% 8.2% 15.9% 21.0% 24.3%

 

34 Source: Energy Information Administration, http://www.eia.gov/naturalgas/data.cfm
3 – 18 

As a conservative assumption, the increase from the period 2005 to 2011 can be compared with 

the longer period of the projected increase in pipeline capacity between 2016 and 2030 in the IPM 

projections for the BB2 BSER scenario where existing natural gas units were required to operate 

at the BSER capacity factor. Increased use of natural gas in existing units can be largely met with 

expansions to existing pipeline facilities and corridors, so that the types of capacity expansion 

required will be less expensive and take less time than new pipelines. Over 2005-2011, the total 

gas deliverability in gas consuming areas increased by 24%. In the BB2 BSER scenario, power 

sector natural gas consumption increased 36% over the base case, and 35% over estimated 2016 

consumption. However, pipeline deliverability increases apply to all sector consumptions, and the 

total consumption in the BSER scenario were projected to increase by 13% over the base case and 

18% over 2016 consumption. Since this historical increase in pipeline capacity over the 2005 to 

2011 period is 24.3%, the historical data indicate that pipeline capacity can expand sufficiently to 

support the potential increased utilization of natural gas power plants quantified for building block 

2 in the BSER modeling. 

Recent pipeline construction continued to demonstrate the pipeline network’s ability to 

accommodate increasing demand for natural gas. According to information released in April, 2014 

by the EIA,35 118 pipeline projects were completed and placed into service from 2010 to 2014, 

totaling 4,699 miles of pipe, and 44,107 MMcf per day of additional pipeline capacity. For projects 

expected to be in service from April, 2014 through 2016, EIA reports 47 projects, with planned 

capacity additions of 20,505 MMcf per day and 1,567 miles of pipe. These projects cover all major 

gas consuming areas of the US, and include both new pipeline construction, such as the Spectra 

Energy’s NEXUS Gas Transmission project in the Upper Midwest and pipeline expansions such 

as the Tennessee Gas Pipeline Project in Connecticut. 

 

35 See www.eia.gov/naturalgas/pipelines/EIA-NaturalGasPipelineProjects.xls. 
3 – 19 

Figure 3-3. Energy Information Administration Natural Gas Pipeline Regions

Source: Energy Information Administration. 

The ability of the pipeline system to accommodate the increased utilization of NGCC quantified 

for building block 2 is reinforced by a recent DOE study of pipeline system expansion.36 This study 

examined two cases of pipeline expansion through 2030, one with a 25% increase in overall natural 

gas demand in 2030 and one with a 46% increase compared to a reference case projection. In a 

key finding, the study concluded that, in both scenarios, compared to historical pipeline 

infrastructure expansion, “Intermediate interstate natural gas pipeline infrastructure needs in [the 

assessed scenarios] are projected to be modest relative to the Reference Case”.37 The levels of 

increased natural gas demand assessed in that DOE study are substantially above the 15% increase 

in natural gas demand projected in EPA’s power sector modeling of a scenario that would realize 

the building block 2 NGCC utilization level (described below). 

 

36 U.S. Department of Energy, Natural Gas Infrastructure Implications of Increased Demand from the Electric Power 

Sector, February 2015. 

37 Ibid. Page vi. 
3 – 20 

3.7 Cost-Effectiveness of Shifting Generation to NGCC 

To evaluate the cost-effectiveness and technical capability of increasing utilization of existing 

NGCC as quantified for building block 2, EPA employed the Integrated Planning Model (IPM), a 

multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector 

that EPA has used for over two decades to evaluate the economic and emission impacts of 

prospective environmental policies. IPM provides a wide array of projections related to the electric 

power sector and its related markets (including least cost capacity expansion and electricity 

dispatch projections) while meeting fuel supply, transmission, dispatch, and reliability constraints. 

Natural gas supply, demand, transportation, storage, and related costs are modeled directly in IPM 

through the incorporation of a natural gas module. The module includes a detail rich representation 

of the natural gas pipeline network inclusive of discount curves that represent the marginal value 

of gas transmission as a function of the pipeline’s load factor. IPM’s natural gas module has the 

capability to expand pipeline capacity on an economic basis. 

At the unit level, IPM contains a detailed representation of new and existing resource options, 

inclusive of key operational limitations. For example, turn down constraints are designed to 

account for the cycling capabilities of EGUs to ensure that the model properly reflects the distinct 

operating characteristics of peaking, cycling, and base load units. EPA believes IPM represents a 

powerful tool to evaluate the technical feasibility of requiring increasing levels of re-dispatch from

higher to lower-emitting EGUs. 

 EPA used IPM to model a series of scenarios representing replacement of fossil steam generation 

in each interconnection with increases of average NGCC net capacity factors in the same 

interconnection of up to 70, 75, and 80 percent. For example, in the 70 percent net capacity factor 

scenario, total fossil fuel-fired generation at existing sources across each interconnection is held 

constant with the level projected in the base case, while the model increases existing NGCC 

utilization in each interconnection either to a 70 percent capacity factor on average, or to a lower 

utilization level if such a level would deliver the same amount of fossil fuel-fired generation 

projected from existing sources in that interconnection in the base case.38

 

The costs and economic impacts of these scenarios were evaluated by comparing the total costs 

and emissions from each scenario to the costs and emissions from the base case. For the scenarios 

reflecting a 70, 75, and 80 percent NGCC utilization rate, comparison to the base case indicates 

that the average cost of CO2 reductions was $15, $24, and $33 per short ton of CO2, respectively, 

over the 2022-2030 period. 

 

38 The existing source fossil fuel-fired generation described here includes generation from existing NGCC, coal 

steam, IGCC and oil/gas steam boilers greater than 25 MW. 
3 – 21 

Table 3-7: IPM Results 

Existing NGCC 

Average 

Capacity 

Factor 

Average Cost ($ 

per short ton, 

2022-2030) 

Base Case N/A 

70% $15 

75% $24 

80% $33 

3.8 Natural Gas Price Impacts 

The extent of generation shifting estimated in this building block can be achieved without causing 

significant economic impacts, even without the gradual phase-in of this building block that was 

not represented in this analysis. For example, in the 75% scenario, delivered natural gas price 

projections increase by approximately 7 percent in the 2022-2030 period, which is well within the 

range of historical natural gas price volatility. For example, the year-to-year percentage difference 

in Henry Hub prices reported by the Energy Information Administration averaged 18.5% over the 

period from 1981 to 2012.39 Projected wholesale electricity price increases over the same period 

were about 4 percent in the 75% case, which is similarly well within the range of historic electric 

price variability. For example, the average year-to-year price change for all of the ISOs and RTOs 

in the East was about 20 percent over the period from 2000 to 2013.40 

 

39 http://www.eia.gov/dnav/ng/hist/rngwhhdA.htm

40 ISO Real-Time data for all hours, from Ventx Velocity Suite data across Eastern ISOs (PJM, NYISO,ISO-NE and 

Midcontinent ISO). 
3 – 22 

Table 3-8: National Average Delivered Natural Gas Price, 

Power Sector (Average 2022-2030) 

Existing NGCC 

Average Capacity 

Factor 

Price 

($/mmBtu) 

% Change 

from Base 

Case 

Base Case $5.45 N/A 

70% $5.70 5% 

75% $5.85 7% 

80% $5.99 10% 
4 – 1 

CHAPTER 4: CLEANER GENERATION SOURCE 

4.1 Introduction 

Renewable energy is a cost-effective approach for reducing carbon dioxide (CO2) emissions from 

fossil fuel-fired electric generating units (EGUs) through the replacement of electricity generated 

from affected EGUs by renewable resources, referred to in this document as renewable energy 

(RE). The portfolio of available RE sources encompasses a wide variety of technologies fueled by 

the sun, wind, water, organic matter, and other resources regularly replenished by physical and 

biological cycles. To integrate the rapidly increasing and evolving portfolio of RE into the Best 

System of Emission Reductions (BSER), EPA has developed a methodology that is based on 

historical data. 

This chapter serves to complement the description of incremental RE as a component of the BSER 

in section V.E of the preamble and is organized into several sections: 

• Section 4.2 provides data and examples to support the seven steps described in the 

preamble to quantifying RE generation potential for the BSER 

• Section 4.3 describes the interaction between the building block 3 generation levels and 

the goal-setting methodology 

• Section 4.4 compares the final building block 3 generation level against the proposed levels 

• Section 4.5 details the NREL’s 2015 Annual Technology Baseline mid-case estimates 

• Section 4.6 explores the technical feasibility and cost effectiveness of the building block 3 

generation level 

4.2 Approach to Quantifying RE Generation Potential for the BSER 

The methodology used to quantify building block 3 generation for the BSER, as described in 

section V.E.6 of the preamble, contains seven steps: 

1. Identify historical maximum capacity change and average capacity change from year to 

year over the past five years (2010 – 2014) for utility-scale solar photovoltaic (PV),1

concentrating solar power (CSP), onshore wind, hydropower, and geothermal 

technologies. 

2. Assign each RE technology an annual capacity factor that represents expected generation 

from each megawatt installed in the future. 

 

1

 In their Solar Market Insight reports, Greentech Media and SEIA define utility-scale by offtake arrangement rather 

than by project size: any project owned by or that sells electricity directly to a utility (rather than consuming it on 

site) is considered a “utility-scale” project. This definition includes even relatively small projects (e.g., 100 kW) that 

sell electricity through a feed-in tariff (“FIT”) or avoided cost contract.”(See Bolinger, M.; Weaver, S.; "Utility 

Scale Solar 2013." Defining "Utility-Scale," LBNL, Sept. 2014. Page 3. http://emp.lbl.gov/publications/utility-scalesolar-2013-empirical-analysis-project-cost-performance-and-pricing-trends) 
4 – 2 

3. Use the data from steps 1 and 2 to produce the annual generation change associated with 

the historical average and historical maximum RE capacity changes for each technology. 

4. Establish an initial level of incremental generation from the building block 3 RE 

technologies that could be expected by 2022 even in the absence of the rule, using EPA’s 

IPM Base Case. 

5. Add the generation associated with the historical average capacity change to the initial 

level to obtain the building block 3 generation level for 2022. To that 2022 level, add the 

generation associated with the historical average capacity change to obtain the building 

block 3 generation level for 2023. 

6. For each subsequent year, add the generation associated with the historical maximum 

capacity change to the building block 3 generation level calculated for the preceding year, 

in order to obtain the building block 3 generation level for each year from 2024 through 

2030. 

7. Apportion the national totals calculated in steps 5 and 6 to each of the three 

interconnections for inclusion in the BSER.2

4.2.1 Step 1 

EPA relied on numerous data sources to compile the most recent five years of capacity deployment 

data for each of the RE technologies that contribute to building block 3 generation.3

 The totals in 

Table 4-1 represent the net change in capacity from year to year, inclusive of declines in operating 

capacity. 

Table 4-1: Annual Capacity Change by RE Technology (MW) 

RE 

Technology 

2010 2011 2012 2013 2014 Average Maximum

Solar PV4 267 784 1,803 2,847 3,934 1,927 3,934 

CSP 78 0 0 410 767 251 767 

Onshore 

Wind 

5,112 6,816 13,131 1,087 4,854 6,200 13,131 

Geothermal 15 138 147 407 4 142 407 

Hydropower 294 -10 47 216 158 141 294 

Note: All values are rounded to the nearest MW. 

 

2

 Unless otherwise indicated, “national” in this chapter refers to the contiguous U.S. 

3

 Energy Information Administration, Geothermal Energy Association, Lawrence Berkley National Laboratory, 

Solar Energy Industries Association/GTM Research, Interstate Renewable Energy Council, American Wind Energy 

Association (2014 onshore wind), and Federal Energy Regulatory Commission (2014 hydropower) 

4

 Solar PV capacity totals are for utility-scale solar PV installations only and are expressed in terms of MWDC
4 – 3 

4.2.2 Step 2 

The NREL Annual Technology Baseline (ATB) estimates serve as the basis for the expected future 

generation by MW of installed capacity for each RE technology:5

• Solar PV – Simple average of all three annual capacity factor values presented in the 

ATB (14%, 20%, and 28%).6

• CSP – Simple average of representative plant performance with six hours of thermal 

electric storage across Solar Class 1, 3, and 5 (28%, 37%, and 38%, respectively). 

• Onshore Wind – Simple average of annual capacity factors for each of the five technoresource groups (32.0%, 37.9%, 44.0%, 46.3%, and 49.0%). 

• Geothermal – Simple average of annual capacity factors for both geothermal 

technology types (90% flash; 80% binary) 

• Hydropower – Simple average of annual capacity factors for each technology (nonpower dams and new stream-reach development) and resource combination (58% - 

67%). 

The capacity factors displayed in Table 4-2 below are assumed to be constant over time for the 

purposes of calculating building block 3 generation totals from 2022-2030. 

Table 4-2: Annual Net Capacity Factor by Technology Type 

RE Technology Capacity Factor 

Solar PV 20.7% 

CSP 34.3% 

Onshore Wind 41.8% 

Geothermal 85.0% 

Hydropower 63.8% 

 

5

 http://www.nrel.gov/analysis/data_tech_baseline.html 

6

 The capacity factor values presented for solar PV account for the range of latitude across the contiguous U.S. Solar 

PV capacity factors are influenced by hourly solar profile, technology type, axis type, inverter losses due to 

conversion from DC to AC power, and other factors. 
4 – 4 

4.2.3 Step 3 

The change in generation, expressed in MWhs, associated with the average and maximum capacity 

changes are the result of multiplying the corresponding capacity values by 8,760 hours/year and 

the net annual capacity factor for each technology, as shown in Table 4-3.

Table 4-3: Annual Generation Change Associated with 

Average and Maximum RE Capacity Changes 

 Average 

Capacity 

Change 

(MW) 

Capacity 

Factor 

Generation 

(MWh, 

Average 

Capacity 

Change) 

Maximum 

Capacity 

Change 

(MW) 

Capacity 

Factor 

Generation 

(MWh, 

Maximum 

Capacity 

Change) 

Solar PV7 1,927 20.7% 3,494,268 3,934 20.7% 7,133,601 

CSP 251 34.3% 754,175 767 34.3% 2,304,590 

Onshore 

Wind 

6,200 41.8% 

22,702,416 

13,131 41.8% 

48,081,520 

Geothermal 142 85.0% 1,057,332 407 85.0% 3,030,522 

Hydropower 141 63.8% 788,032 294 63.8% 1,643,131 

Total 

Generation 

(MWh) 

 

28,796,222 

 

62,193,363 

 

4.2.4 Step 4 

Initial incremental RE generation totals for utility-scale solar PV, CSP, onshore wind, geothermal, 

and hydropower are taken from EPA IPM Base Case.8

 Initial incremental RE represents generation 

from existing projects currently in commercial operation with an online year of 2013 or later; 

projects that are not currently operating but are firmly anticipated to be operational in the future 

and have either initiated construction or secured financing; and capacity that is projected to deploy 

as an economic resource to meet load. All of this generation is represented in EPA IPM Base Case 

projections. 

To quantify an initial generation value, EPA interpolated between EPA IPM Base Case’s 2020 and 

2025 projections, assuming each calendar year accounts for 20% of the difference in those 

projections (i.e., a linear interpolation between run years). Given that the projected incremental 

 

7

 Solar PV capacity totals are for utility-scale solar PV installations only and are expressed in terms of MWDC

8

 Documentation and modeling files are available in the docket for this rulemaking and online at 

www.epa.gov/powersectormodeling. 
4 – 5 

RE generation across the contiguous U.S. in 2020 is 202,219,818 MWh, and the corresponding 

projected value in 2025 is 256,541,353 MWh, the initial generation value is calculated as: 

256,541,353 − 	202,219,818

5

+ 202,219,818 = 213,084,125

This initial level represents expected incremental RE generation before the assumed deployment 

of additional RE capacity in 2022 and beyond, as described in the following steps. 

4.2.5 Step 5 

The national building block 3 generation total in 2022 is the initial level (step 4) plus the generation 

associated with the historical average change in capacity (step 3): 

• 2022 National Building Block 3 Generation = 213,084,125 MWh + 28,796,222 MWh = 

241,880,347 MWh 

The 2023 national building block 3 generation value is the sum of the 2022 value plus the 

generation associated with the historical average change in capacity: 

• 2023 National Building Block 3 Generation = 241,880,347 MWh + 28,796,222 MWh = 

270,676,570 MWh9

4.2.6 Step 6 

The 2024 national building block 3 generation value is the sum of the 2023 value plus the 

generation associated with the historical maximum change in capacity (step 3): 

2024 National Building Block 3 Generation = 

270,676,570 MWh + 62,193,363 MWh = 332,869,933 MWh 

For each subsequent year through 2030, the national building block 3 generation total is the 

previous year’s value plus the generation associated with the historical maximum change in 

capacity (62,193,363 MWh). The annual values for all years are presented below in Table 4-4: 

 

9

 Values may not sum due to rounding; refer to GHG Mitigation Measures spreadsheet for unrounded values. 
4 – 6 

Table 4-4: National Building Block 3 Generation Totals (MWh) 

Year Building Block 3 

Generation 

2022 241,880,347 

2023 270,676,570 

2024 332,869,933 

2025 395,063,296 

2026 457,256,659 

2027 519,450,023 

2028 581,643,386 

2029 643,836,749 

2030 706,030,112 

4.2.7 Step 7 

To apportion the national building block 3 generation totals calculated in steps 5 and 6, EPA 

considered the geographic pattern of RE deployment in an IPM modeling scenario projecting 

national-level incremental generation for 2025 and 2030 at levels similar to the building block 3 

generation totals.10 The scenario included a representation of utility-scale solar PV, CSP, onshore 

wind, geothermal, and hydropower generation. The geographic pattern by interconnection of 

incremental RE generation totals for 2020, 2025, and 2030 are displayed below: 

Table 4-5: Incremental RE Generation by Interconnection 

from Building Block Assignment Analysis (MWh) 

Interconnection 2020 2025 2030 

Eastern 165,133,240 272,168,303 449,874,640 

Western 53,558,376 76,325,304 165,543,643 

Texas 20,146,141 63,283,451 109,380,771 

The absolute generation levels in this projection do not substitute for the national building block 3 

generation totals in Table 4-4; instead, this step is only assessing the most economic location by 

interconnection of this general level of incremental RE generation potential. To facilitate this 

geographic assignment of the building block 3 generation totals in Table 4-4, the modeled 

generation totals for each interconnection are converted into a percentage of the modeled national 

total such that these interconnection-specific percentage values, or “shares”, can be applied to 

 

10 The IPM run ‘Building Block 3 Generation Assignment’ is available in the docket 
4 – 7 

apportion the building block 3 generation levels. The interconnection shares by model projection 

year are shown below in Table 4-6. 

Table 4-6: Incremental RE Generation Shares by Interconnection 

from Building Block Assignment Analysis 

Interconnection 2020 2025 2030 

Eastern 69.2%11 66.1% 62.1% 

Western 22.4% 18.5% 22.8% 

Texas 8.4% 15.4% 15.1% 

The model run-year values are then converted into values for each year from 2022 through 2030 

using a linear interpolation of the model-projected 2020 and 2030 generation share values. The 

resulting shares by interconnection for each year are shown in Table 4-7 below. 

Table 4-7: Building Block 3 Generation Shares by Interconnection 

 Eastern Western Texas 

2022 67.8% 22.5% 9.7% 

2023 67.1% 22.5% 10.4% 

2024 66.4% 22.6% 11.1% 

2025 65.7% 22.6% 11.8% 

2026 64.9% 22.6% 12.4% 

2027 64.2% 22.7% 13.1% 

2028 63.5% 22.7% 13.8% 

2029 62.8% 22.8% 14.4% 

2030 62.1% 22.8% 15.1% 

Applying the generation shares in Table 4-7 to the national building block 3 generation totals in 

Table 4-4 produces an annual incremental generation value for each interconnection in each year, 

as shown in Table 4-8. 

 

11 Value rounded up to ensure all interconnections sum to 100% 
4 – 8 

Table 4-8: Annual Average Incremental Generation Values, 

Eastern Interconnection (MWh) 12

 National 

Building 

Block 3 

Generation 

Total 

Eastern 

Interconnection 

Generation 

Share (%) 

Eastern 

Interconnection 

Generation 

Share (MWh) 

Annual 

Increase in 

Generation 

Values13

Multi-Year 

Average 

Annual 

Increase in 

Generation 

Values 

2022 241,880,347 67.8% 163,946,499 12,983,007 15,289,641 

2023 270,676,570 67.1% 181,542,775 17,596,276 

2024 332,869,933 66.4% 220,892,487 39,349,712 

36,700,275 

2025 395,063,296 65.7% 259,359,054 38,466,566 

2026 457,256,659 64.9% 296,942,475 37,583,421 

2027 519,450,023 64.2% 333,642,749 36,700,275 

2028 581,643,386 63.5% 369,459,879 35,817,129 

2029 643,836,749 62.8% 404,393,862 34,933,983 

2030 706,030,112 62.1% 438,444,700 34,050,838 

As shown in the steps above, this approach considers annual generation levels associated with the 

historical average RE capacity change to inform building block 3 levels in 2022 and 2023, and the 

historical maximum RE capacity change to inform building block 3 levels in 2024 through 2030. 

In keeping with that schedule, multi-year average annual incremental generation values are 

presented in Table 4-8 for 2022-2023 and for 2024-2030 to create an interconnection-specific 

analog to the national values presented above in step 3. For example, the 2022-2023 average annual 

incremental generation value for the Eastern Interconnection (using the values in Table 4-8) is 

15,289,641 MWh, which is the Eastern Interconnection’s share of the national annual incremental 

generation value of 28,796,222 MWh (based on the historical average RE capacity change) 

presented in step 3. Following the same process, the 2024-2030 average annual incremental 

generation value for the Eastern Interconnection is 36,700,275 MWh, which is the Eastern 

Interconnection’s share of the national annual incremental generation value of 62,193,363 MWh 

(based on the historical maximum RE capacity change) presented in step 3. 

The final step in calculating interconnection-level building block 3 generation totals is to apply 

each region’s average incremental generation to its share of the initial generation level presented 

in step 4. To continue the Eastern Interconnection example: 

 

12 Table 4-8 displays the Eastern Interconnection; refer to the GHG Mitigation Measures spreadsheet for the 

Western and Texas Interconnections calculations: 

13 These amounts are in addition to the initial generation value for the Eastern Interconnection, which is 150,963,493 

MWh. 
4 – 9 

• 2022 Building Block 3 Generation Level = Initial Generation Level + 2022-2023 

Average Incremental Generation = 150,963,493 MWh + 15,289,641 MWh = 166,253,134 

MWh 

• 2023 Building Block 3 Generation Level = 2022 Building Block 3 Generation Level + 

2022-2023 Average Incremental Generation = 166,253,134 MWh + 15,289,641 MWh = 

181,542,775 MWh 

For all subsequent years, 2024 through 2030, the building block generation level for the Eastern 

Interconnection is the previous year’s generation level plus the 2024-2030 average incremental 

generation level of 36,700,275 MWh. The calculations for all years and interconnections are 

available in the GHG Mitigation Measures spreadsheet. 

The building block 3 generation levels used in the BSER for each interconnection are displayed 

below in Table 4-9: 

Table 4-9: Building Block 3 Generation Levels by Interconnection (MWh) 

 Eastern 

Interconnection

Western 

Interconnection

Texas 

Interconnection

Total 

2022 166,253,134 56,663,541 18,963,672 241,880,347 

2023 181,542,775 60,956,363 28,177,431 270,676,570 

2024 218,243,050 75,244,721 39,382,162 332,869,933 

2025 254,943,325 89,533,078 50,586,893 395,063,296 

2026 291,643,600 103,821,436 61,791,623 457,256,659 

2027 328,343,875 118,109,793 72,996,354 519,450,023 

2028 365,044,150 132,398,151 84,201,085 581,643,386 

2029 401,744,425 146,686,508 95,405,816 643,836,749 

2030 438,444,700 160,974,866 106,610,547 706,030,112 

4.3 Building Block 3 Generation Required by the Goal-Setting 

As discussed in section V.A.3.f and section VI of the preamble, the BSER reflects the degree of 

emission limitation achieved through application of the building blocks in the least stringent 

region. Therefore, by definition, there is an amount of building block 3 generation in each year 

that is not required to achieve the goal rates. Through the application of a minimization routine 

described in the CO2 Emission Performance Rate and Goal Computation TSD, EPA has solved for 

the minimum amount of building block 3 generation necessary in each interconnection for affected 

EGUs to achieve the source category-specific emission performance rates. 
4 – 10 

Table 4-10: National Building Block 3 Generation Necessary to Achieve Source CategorySpecific Emission Performance Rates (MWh) 

Year Building Block 3 

Generation 

Building Block 3 

Generation 

Required to 

Achieve Goal Rates 

2022 241,880,347 146,904,585 

2023 270,676,570 179,963,324 

2024 332,869,933 239,903,903 

2025 395,063,296 292,428,842 

2026 457,256,659 346,222,749 

2027 519,450,023 405,981,689 

2028 581,643,386 449,706,611 

2029 643,836,749 493,669,241 

2030 706,030,112 539,774,619 

The building block 3 generation levels, both total and required to achieve the goal rates, are 

presented together with the projections of incremental RE from EPA’s IPM Base Case: 

Figure 4-1: Building Block 3 Generation Levels, Relative to Base Case Incremental RE 

(MWh) 

 -

 100,000,000

 200,000,000

 300,000,000

 400,000,000

 500,000,000

 600,000,000

 700,000,000

 800,000,000

2022 2023 2024 2025 2026 2027 2028 2029 2030

Base Case Incremental RE

Building Block 3 Generation

Building Block 3 Generation Necessary to Achieve Goal Rates
4 – 11 

Figure 4-1 displays how the combination of building block 3 and the goal-setting methodology 

serve to significantly reduce compliance pressure on affected EGUs in the near-term, insulating 

near-term building block 3 generation levels against adjustments to RPS requirements and 

fluctuations in market conditions, while still establishing an historically-based deployment 

pathway that achieves significantly greater amounts of RE through 2030 than what is projected in 

the Base Case. The interaction between the building block 3 generation level, goal-setting 

methodology and the technical feasibility and cost-effectiveness of incremental RE is explored in 

greater detail in section 4.6. 

4.4 Building Block 3 Generation Levels, Relative to Proposed Levels 

The final methodology, which is adapted from the proposal’s alternative approach, produces 

greater incremental RE generation than the proposed levels. For example, the 2030 building block 

3 generation level of 706,030 GWh in the final rule is 370,660 GWh greater than the incremental 

RE contained in the proposal’s alternative approach at a constant $/ton cost-effectiveness.14 This 

difference is comprised primarily of two components – removal of the benchmark development 

rate15 from the proposed approach and updating RE cost and performance data to the most recent 

NREL data. The magnitude of these components is shown below: 

Figure 4-2: Proposed and Final Building Block 3 Generation Levels, by Component in 2030 

 

14 The proposed RE levels were associated with a $36/ton cost; the final building block 3 generation levels produce 

an average cost of $37/ton (see section 4.6). 

15 The benchmark development rate set limits on the projected economic deployment of RE if a state had achieved 

deployment levels that met a benchmark deployment rate (total capacity/total resource) equal to the top-third of 

states. See the response to comment document for more detail. 
4 – 12 

EPA invited comment at proposal on RE levels in excess of the final building block 3 generation 

level. For example, the Alternative RE Approach TSD cited NREL values for incremental onshore 

wind, solar and geothermal technologies of 687,600 GWh.16 When combined with the proposed 

levels of incremental hydropower (102,524 GWh), the resulting generation level of 790,124 GWh 

exceeds the building block 3 generation level in the final rule by more than 80 TWh in 2030. 

Additionally, the goal-setting methodology in the final rule does not require all of the building 

block 3 generation level to achieve the goal rates (see Section 4.3). In evaluating the proposed and 

final levels of building block 3 generation, it is appropriate to compare the amount of building 

block 3 generation levels necessarily reflected in the final goal rates: 

Figure 4-3: Block 3 Generation Levels Necessary to Achieve Goal Rates 

and Alternative NREL Values, in 2030 

It should be noted that the building block 3 generation levels from the proposal and the final rule 

produce consistent estimates of cost-effectiveness - $36/ton at proposal versus $37/ton in the final 

rule. Preserving a consistent cost-effectiveness determination at increased levels of RE penetration 

reflects the final rule’s adoption of updated cost and performance assumptions from the 2015 

NREL Annual Technology Baseline (ATB). This update is described in detail in section 4.5 below. 

4.5 NREL’s 2015 Annual Technology Baseline Mid-Case Estimates 

EPA’s approach for determining the building block 3 component of the BSER is based in part on 

revised cost and performance estimates for onshore wind and solar technologies. As discussed in 

section V.E.6 of the preamble to final rule, commenters provided data demonstrating that the cost 

and performance estimates relied on in the proposed rule did not reflect the decline in cost and 

 

16 http://www2.epa.gov/cleanpowerplan/clean-power-plan-proposed-rule-alternative-renewable-energy-approach 
4 – 13 

increase in performance that have been demonstrated by current projects, particularly in regards 

to wind and solar. As a result, the agency revised its data for onshore wind and solar technologies 

in the final rule. The revision included the mid-case estimates from the National Renewable Energy 

Laboratory’s (NREL) 2015 Annual Technology Baseline (ATB). 

EPA selected the NREL 2015 ATB mid-case estimates based on the quality of its data and 

consistency with recent RE cost and performance trends. The ATB capital cost estimates cover the 

same scope of power plant envelope (equipment, installation, developer costs, etc.) as used for 

AEO inputs used in the proposal. The agency used the draft 2015 ATB version, which was 

available at the time of the analysis. Recently, the final 2015 version was posted.17 It is particularly 

notable that the final 2015 version’s cost estimates are either unchanged or lowered.18 Despite the 

recent availability of the final 2015 version, the agency believes its data is well supported by the 

draft 2015 version’s cost and performance data.

In comparing the AEO2013 data set used in the proposal to the NREL ATB data set used in the 

final rule, EPA found AEO2013 installation cost assumptions for onshore wind in 2014 are 30% 

higher than the ATB mid-case cost assumptions for the same year.19 Moreover, the AEO2013 

assumes onshore wind installation costs in 2030 at $2,100/kW, which is almost 30% higher than 

installation costs in the ATB mid-case in that year. Installation costs for solar PV in 2013 for the 

AEO2013 are 15% higher than those assumed in the ATB mid-case. 20 Similarly, installation cost 

assumptions for solar PV in 2030 are almost 75% higher in the AEO2013 cost assumptions when 

compared to the NREL ATB Mid RE Case. 

 

17 Available at http://www.nrel.gov/analysis/data_tech_baseline.html. 

18 NREL lowered current and projected overnight capital cost values for the ATB mid-case solar PV projection to 

reflect significant changes in solar market prices that has occurred over the last year. NREL, Annual Technology 

Baseline (ATB) Spreadsheet, July 2015, see “Solar – PV” worksheet, Available at: 

http://www.nrel.gov/docs/fy15osti/64077-DA.xlsm. 

19 EIA capital costs sourced from Assumptions to the Annual Energy Outlook 2013, Table 8.2 (pp 103), Available 

at: http://www.eia.gov/forecasts/aeo/assumptions/pdf/0554(2013).pdf. Cost projections for future years were 

obtained from personal communications with EIA. 

20 The AEO2015 contains updated capital costs for wind and solar PV which are lower than the AEO2013, reflecting 

the decrease in costs over the previous two years. Despite the lower capital costs in AEO2015, the NREL 2015 ATB 

estimates were lower and more consistent with recent RE cost and performance trends. 
4 – 14 

Table 4-11: Comparison of Onshore Wind and Utility-Scale Solar PV 

Cost Estimates in AEO2013 and ATB Mid-Case

 

In comparing the two data sets with current project costs, recent trends, and a reasonable 

expectation of the future, EPA found that the ATB mid-case estimates are more in line with current 

costs and recent market analysis and projections than the AEO2013 costs. 

The rapid cost declines for wind and solar have been well documented. Lazard’s recent Levelized 

Cost of Energy Analysis found that wind and solar PV became increasingly competitive over the 

last five years. During that time period, the average percentage decrease of high and low of the 

LCOE ranges for wind and utility-scale solar PV were 58 percent and 78 percent, respectively.21

Lazard’s capital costs for wind and utility-scale solar PV were $1,400-$1,800/kW and $1,500-

$1,750/kWDC, respectively.22 These cost estimates were significantly lower than near term and 

longer term AEO forecasts. 

EPA also observed solar PV costs from other data sources that are considerably lower than those 

reported in the AEO2013, and more consistent with those seen in ATB mid-case. For instance, the 

U.S. Solar Market Insight report published for Q1 2015 shows recent utility-scale solar installed 

costs below $2,000/kWDC, whereas the AEO2013 assumes costs above $2,800/kWDC for the year 

2015.23 Similarly, a recent report by Lawrence Berkley National Lab (LBNL) shows that utilityscale solar PV projects were installed in 2013 at average costs of $3,000/kWDC.

24 While these 

recent projects may have represented best-in-class installations for the years 2013-2014, it is 

 

21 Lazard, Levelized Cost of Energy Analysis-Version 8.0, September 2014, p. 9, Available at: 

http://www.lazard.com/media/1777/levelized_cost_of_energy_-_version_80.pdf. 

22 Levelized Cost of Energy Analysis-Version 8.0, Lazard, September 2014, pp. 16-17, Available at: 

http://www.lazard.com/media/1777/levelized_cost_of_energy_-_version_80.pdf. 

23 U.S. Solar Market Insights Report, Solar Energy Industries Association/GTM Research, Q1 2015, figure 2.4, 

Available at: http://www.seia.org/research-resources/solar-market-insight-report-2015-q1. 

24 Is $50/MWh Solar for Real? Falling Project Prices and Rising Capacity Factors Drive Utility-Scale PV Toward 

Economic Competitiveness (2015). LBNL. Available at: http://emp.lbl.gov/sites/all/files/lbnl-183129.pdf (referenced 

figure 4). 

Technology Report 2013 2014 2015 2020 2025 2030

Onshore Wind AEO 2013 $2,246 $2,274 $2,330 $2,293 $2,192 $2,106

ATB - TRG 3 Mid $1,758 $1,729 $1,721 $1,674 $1,643 $1,630

Utility Solar PV AEO 2013 ($/kW-DC) $3,144 $2,932 $2,881 $2,658 $2,501 $2,363

ATB - Mid ($/kWDC) $2,673 $2,522 $2,369 $1,603 $1,470 $1,337

Notes

Capital Costs (2013$/kW)

1. The AEO2013 costs are in 2011$, whereas in ATB they are in 2013$. For the comparison, 

the AEO2013 costs were inflated using a net inflation of 3.28% to make them directly comparable.

2. The costs for solar PV in AEO are in $/kW-AC, whereas in ATB they are in $/kW-DC. For the 

comparison, AEO costs were expressed in $/kW-DC, using EIA's assumed 1.25:1 ratio, which was 

presented as a rounded value in EIA's Updated Capital Cost Estimates for Utility Scale Electricity 

Generating Plants (See p. 24-2), according to email communications with EIA staff. 
4 – 15 

reasonable to assume that average costs by 2020 will generally reflect these best-in-class 

installations. In contrast, the AEO2013 assumes a more conservative cost decline, and is close to 

$2,600/kWDC by 2020, and about $2,300/kWDC by 2030. 

Table 4-12: Comparison of Utility-Scale Solar PV Cost Estimates 

The ATB near-term capital cost estimates for CSP with storage are comparable with Lazard’s. The 

ATB assumes a CSP trough with six and twelve hours of storage ($6,777-$8,532/kW) and Lazard 

examines a tower with ten to eighteen hours of storage ($7,000-$9,800/kW). While the 

configurations that serve as the basis for the estimates differ, they are close enough to validate the

ATB cost estimates for CSP. Additionally, the projections were based on SunShot Vision study 

and vetted with solar industry participants. 

Similarly, the recent wind costs estimates are also much lower than those assumed in the 

AEO2013. The LBNL 2013 Wind Technologies Report showed 2013 capacity-weighted average 

installed costs for onshore wind at $1,630/kW, whereas the AEO2013 assumes costs close to 

$2,250/kW (2013$) for the same year.25 Furthermore, the AEO2013 wind cost in 2030, which 

assumes continued cost reductions, is about $2,100/kW. Yet, this wind cost in 2030 is almost 30 

percent higher than observed wind costs in 2013.26

 

25 2013 Wind Technologies Market Report, LBNL, August 2014, Available at: 

http://emp.lbl.gov/sites/all/files/2013_Wind_Technologies_Market_Report_Final3.pdf (referenced Figure 39 on 

Page 49). 

26 Although the LBNL sample size is small for 2013, the decline in wind installed costs in 2013 from 2012 is in 

keeping with the decline in wind turbine prices, which is a leading indicator of installed cost trends. 2013 Wind 

Technologies Market Report, LBNL, August 2014, Available at: 

http://emp.lbl.gov/sites/all/files/2013_Wind_Technologies_Market_Report_Final3.pdf. 

Technology Report 2013 2014 2015 2020 2025 2030

Utility Solar PV AEO 2013 ($/kWDC) $3,144 $2,932 $2,881 $2,658 $2,501 $2,363

ATB - Mid ($/kWDC) $2,673 $2,522 $2,369 $1,603 $1,470 $1,337

LBNL ($/kWDC) $3,000

SEIA/GTM ($/kWDC) $2,000

Lazard (2014$) $1,500-$1,750

Notes

Capital Costs (2013$/kW)

1. The AEO2013 costs are in 2011$, whereas in ATB they are in 2013$. For the comparison, 

the AEO2013 costs were inflated using a net inflation of 3.28% to make them directly comparable.

3. While all sources are reported as ‘2013’, there is a difference what each source defines as ‘year’.

For example, the LBNL report and the ATB represent the projects completed installation in 2013 

while the AEO numbers are for projects that were initiated in 2013.

2. The costs for solar PV in AEO are in $/kW-AC, whereas in ATB they are in $/kW-DC. For the 

comparison, AEO costs were expressed in $/kW-DC, using EIA's assumed 1.25:1 ratio, which was 

presented as a rounded value in EIA's Updated Capital Cost Estimates for Utility Scale Electricity 

Generating Plants (See p. 24-2), according to email communications with EIA staff. 
4 – 16 

Table 4-13: Comparison of Onshore Wind Capital Cost Estimates 

Commenters (see RTC Chapter 3.3.7, Cost Effectiveness of RE) said that the proposal did not 

capture the full potential of RE and that the final rule needs to reflect the dramatic cost reductions

that have occurred in recent years. There was support for the principle of using conservative 

assumptions, but significant concern about the use of cost and performance assumptions that are 

more pessimistic than currently observable and accurate data on the cost of RE deployment. They 

also added that EPA's analysis for the proposed rule using AEO2013 did not account for the cost 

reductions that can reasonably be expected to continue. For projecting future economics and 

performance, commenters recommended reliance on credible projections available from the DOE 

and National Laboratories. They asserted that recent cost declines are also consistent with the 

projected declines expected as an industry experiences significant growth, which the RE industry 

has seen in the U.S. and abroad. 

Commenters provided a number of references to data from a variety of sources (e.g., LBNL, NREL 

and Lazard) to support their arguments that the agency needs to change cost assumptions. A 

number of commenters noted NRDC’s recent analysis that used a number of these sources to show 

AEO2013's cost estimates for wind and solar energy are 46 percent above current average costs.27

Commenters also contrasted Lazard’s current range of levelized costs of energy (LCOE) for 

onshore wind without subsidies of $37-$81/MWh with AEO2013's 2019 estimates of $70-

90/MWh.28 Regarding solar PV, commenters highlight DOE and national laboratory reports that 

have observed the cost of building utility-scale solar projects has declined by about 50 percent 

between 2010 and 2014. The commenters said these declines are consistent with NREL's modeled 

prices using its bottom-up modeling methodology - NREL estimates that the price of solar declined 

to $1800/kWDC in Q4 2013. Commenters also highlight forecasted solar PV cost estimates and 

found no reason to believe that the declines in cost will not continue. Referencing the SunShot 

 

27 NRDC, Issue Brief: EPA's Clean Power Plan Could Save Up to $9 Billion in 2030, November 2014, Available at: 

http://www.nrdc.org/air/pollution-standards/files/clean-power-plan-energy-savings-IB.pdf. 

28 Lazard, Levelized Cost of Energy Analysis-Version 8.0, September 2014, p. 15, Available at: 

http://www.lazard.com/media/1777/levelized_cost_of_energy_-_version_80.pdf. 

Technology Report 2013 2014 2015 2020 2025 2030

Onshore Wind AEO 2013 $2,246 $2,274 $2,330 $2,293 $2,192 $2,106

ATB - TRG 3 Mid $1,758 $1,729 $1,721 $1,674 $1,643 $1,630

Lazard (2014$) $1,400-$1,800

LBNL $1,630

Notes

2. While all sources are reported as ‘2013’, there is a difference what each source defines as ‘year’.

For example, the LBNL report and the ATB represent the projects completed installation in 2013 

while the AEO numbers are for projects that were initiated in 2013.

Capital Costs (2013$/kW)

1. The AEO2013 costs are in 2011$, whereas in ATB they are in 2013$. For the comparison, 

the AEO2013 costs were inflated using a net inflation of 3.28% to make them directly comparable.
4 – 17 

Vision study’s roadmap for continued cost declines in solar PV technologies, they observed that 

estimates of solar system costs dropping 75% between 2010 and 2020. They added that NREL 

recently predicted in its 2014 update on Solar PV pricing trends that solar PV costs are still on 

track to meet the SunShot Initiative goal of $1,000/kWDC by 2020 for utility-scale PV. 

It should also be noted that the NREL ATB cost report was published in 2015, whereas the AEO 

costs were published in 2013, and that the most recent AEO2015 includes significant cost 

reductions of wind and solar PV as compared to AEO2013.29 Commenters noted that the LBNL 

found that the AEO2013 assumptions were based on projects completed in 2012, and the installed 

price data for those projects may reflect contract terms agreed to a number of years prior to project 

completion.30 

NREL’s ATB and Standard Scenarios were developed with the objectives of creating consistent 

and normalized technology cost and performance assumptions as well as defining a conceptual 

and consistent scenario framework that can be used in future analyses. 

In general, the ATB cost and performance estimates represents typical U.S. RE plants by reflecting 

a range of resources and groupings of RE technologies, such as wind technology resource groups 

or TRGs based on wind resource and turbine technologies. The future cost projections are 

compared with data in published literature and generally within the bounds of those perspectives. 

These literature estimates were normalized to a common starting point. Projections for each 

technology were developed independently using distinct methods, but the starting points were 

compared with available market data to provide consistent baseline methodology.31

The wind cost projections derived from literature reviews for onshore and offshore wind, including 

numerous projection scenarios from many independently published studies, and vetted with a 

consortium of National Laboratory, DOE and wind industry experts. The results are three different 

projections developed for scenario modeling as bounding levels, based generally on maximum, 

median, and minimal annual cost reductions based on literature. These are reflected as the low, 

mid, and high cost cases, respectively.32

The solar cost projections are based on SunShot Vision study (2012) and vetted broadly with solar 

industry participants. Similar to wind projections, three cost projections are developed for scenario 

modeling as bounding levels. These low, mid, and high cases for utility-scale solar PV are based 

changing cost reduction trajectories. The concentrating solar power (CSP) cases are based on 

 

29 http://www.eia.gov/forecasts/aeo/assumptions/pdf/table_8.2.pdf 

30 Barbose, et al., Tracking the Sun VII, LBNL, September 2014, Available at: 

http://emp.lbl.gov/publications/tracking-sun-vii-historical-summary-installed-price-photovoltaics-united-states1998-20. 

31 ATB Summary Presentation, NREL, March 2015, Available at: 

http://www.nrel.gov/analysis/data_tech_baseline.html. 

32 ATB Summary Presentation, NREL, March 2015, Available at: 

http://www.nrel.gov/analysis/data_tech_baseline.html. 
4 – 18 

differing evolutionary paths for CSP design and deployment. These projections are compared to 

range of available analyst projections from BNEF, EIA, and EREC.33 

4.6 Technical Feasibility and Cost-Effectiveness of the Building Block 3 Generation Levels 

This section addresses the technical feasibility of the RE requirements described in the previous 

sections for this building block. The current deployment and operation of RE resources are 

discussed first, followed by analysis of the future deployment potential for RE and related needs 

such as available transmission capacity to deliver RE. 

4.6.1 Historical Deployment 

The major incremental additions to RE capacity over the past two decades have come from wind 

and solar, although there have also been some additions to hydro and geothermal resources. After 

constituting two or three percent of the total RE capacity in the 1990s, wind and solar capacity 

have increased dramatically to over 50 percent of total RE today, as shown in Figure 4-4: 

 

33 ATB Summary Presentation, NREL, March 2015, Available at: 

http://www.nrel.gov/analysis/data_tech_baseline.html. The cost projection references include: Greenpeace/EREC 

(2014). Energy [R]evolution: A Sustainable USA Energy Outlook. 

http://www.greenpeace.org/usa/Global/usa/planet3/PDFs/Solutions/Energy-Revolution-2014.pdf (utility-scale only); 

International Energy Agency. (2013). World Energy Outlook 2013. 

http://www.worldenergyoutlook.org/publications/weo-2013/. (New Policy & 450 Scenarios for utility-scale & 

commercial-scale); Bloomberg New Energy Finance (2014). Q2 PV Market Outlook; and, United States Energy 

Information Administration (EIA). (2014a). Annual Energy Outlook 2014 with Projections to 2040. DOE/EIA0383(2014). Washington, D.C.: U.S. Department of Energy Office of Integrated and International Energy Analysis. 

http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf
4 – 19 

Figure 4-4: Growth in Utility-Scale U.S. Renewable Resources34

Many states are already integrating significant levels of Variable Generation (VG) RE resources. 

For wind and solar, there were 21 states in 2014 where RE exceeded 5% of the total annual 

generation, and 10 states where RE exceeded 10%.35 Several states were considerably higher: Iowa 

at 28.5% from wind and solar, Kansas 21.7% and South Dakota at 25.3% were all above 20 %, in 

2014 with Idaho not far below at 18.6%. If Geothermal is included, California is also close with 

18.3%. 

The current grid can reliably integrate much higher levels of over short time intervals of an hour 

or a day. ERCOT met 40 percent of demand on 3/31/14 with wind power; SPP met 33 percent on 

4/6/13, and Xcel Energy Colorado 60 percent on 5/2/13. Moreover, many states also demonstrate 

that high levels of wind and solar VG can be sustained for longer periods. Looking at the maximum 

monthly percentage of wind and solar in 2014, nine states are already above 20% and 18 are above 

10% of total generation from wind and solar for at least one month in 2014. Percentages are as 

high as 42.3 in Iowa, 37.1 in South Dakota, and 33.7 in Kansas. These figures, along with many 

other studies36 looking at the integration of variable generation RE, indicate that the current 

 

34 Source: http://www.eia.gov/electricity/data.cfm#generation 

35 EIA, Form EIA-923 and EIA-860 reports. 

36 References to include: Cochran, J., Denholm, P., Speer, B., and Miller, M. Grid Integration and the Carrying 

Capacity of the U.S. Grid to Incorporate Variable Renewable Energy. NREL/TP-6A20-62607, 2015. 

(http://www.nrel.gov/docs/fy15osti/62607.pdf); Hand, M.M.; Baldwin, S.; DeMeo, E.; Reilly, J.M.; Mai, T.; Arent, 

D.; Porro, G.; Meshek, M.; Sandor, D. eds. 4 vols. (2012). Renewable Electricity Futures Study. NREL/TP-6A20-

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Summer Capacity (MW)

Geothermal Solar(Thermal+PV) Wind
4 – 20 

configuration of the electric power system is already capable of integrating very high levels of 

renewable variable generation for sustained periods of time. 

4.6.2 Deployment Projections 

EPA believes the historically grounded Building Block 3 generation levels constitute moderate 

estimates of the future deployment of RE that can be achieved. The moderate nature of these levels 

is demonstrated by: 

• EPA’s analysis of building block 3 generation levels conducted using IPM 

• A multitude of external studies and reports that demonstrate the feasibility of higher 

percentages of total generation than are associated with the building block 3 generation 

levels 

The IPM scenarios support building block 3 generation levels in two ways - by apportioning the 

national-level generation totals calculated from national-level deployment, and validating the 

building block 3 generation levels as technically feasible and cost-effective.37 It is important to 

note that the IPM model was not the basis for the overall setting of the Building Block 3 generation 

levels; as described above, those levels are based on historical additions that have actually been 

achieved. The IPM model, however, is a useful tool for analyzing the cost and impact of deploying 

these historically-based generation levels. IPM is a power sector planning model with a detailed 

representation of current and potential generating resources, a large number of regions, 

interregional transmission constraints based on current limits, and regional reserve margins based 

on current NERC standards. IPM does not attempt to perform a detailed power flow analysis, or 

to project new transmission additions; however, it captures key variables such as costs associated 

with connecting new renewable resource to the grid within a region and will include additional 

costs related to interregional transmission congestion from constraints in the current grid. For these

reasons, IPM is an appropriate tool for analyzing the generation levels assumed for RE and for 

reviewing RE penetration levels and the impact on the overall generation mix.38

To specifically assess the cost effectiveness and technical feasibility of building block 3, EPA 

imposed minimum generation constraints in IPM at levels consistent with the building block 3 

 

52409. Golden, CO. (http://www.nrel.gov/analysis/re_futures/); Bloom, A., Townsend, A., Palchak, D., King, J., 

Ibanez, E., Barrows, C., Hummon, M., Draxl, C., (2015). Eastern Renewable Generation Integration Study. 

NREL/TP-6A20-64472. Golden, CO. In press, http://www.nrel.gov/electricity/transmission/eastern_renewable.html; 

Lew, D., Brinkman, G., Ibanez, E., Florita, A., Heaney, M., Hodge, B.-M., Hummon, M., Stark, G., King, J., Lefton, 

S.A., Kumar, N., Agan, D., Jordan, G., Venkataraman, S., (2013). The Western Wind and Solar Integration Study 

Phase 2. NREL/TP-5500-55588. Golden, CO. (http://www.nrel.gov/docs/fy13osti/55588.pdf); Miller, N.W., Shao, 

M., Pajic, S., D’Aquila, R., (2014). Western Wind and Solar Integration Study Phase 3 – Frequency Response and 

Transient Stability. NREL/SR-5D00-62906. Golden; (http://www.nrel.gov/docs/fy15osti/62906.pdf); PJM 

Renewable Integration Study. 2014. (http://www.pjm.com/committees-and-groups/subcommittees/irs/pris.aspx) 

37 Supporting IPM analysis, including ‘BB3: Generation Assignment’ and ‘BB3: Cost-Effectiveness’ are available in 

the docket. 

38 For further documentation detail on the IPM model, visit http://www.epa.gov/powersectormodeling. 
4 – 21 

generation levels. The only technologies made available to satisfy these constraints were the RE 

technologies associated with building block 3 – utility-scale solar PV, CSP, onshore wind, 

geothermal, and hydropower with an online year of 2013 or later. 39 To meet these levels in a 

manner that reflects the BSER, every incremental MWh of qualifying RE the model deployed 

necessitated that one MWh of affected EGU generation be reduced.40 This requirement was 

necessary to reflect that building block 3 is premised on the replacement of affected EGU 

generation by incremental RE. The cost effectiveness of meeting this constraint, expressed as the 

average $/ton of CO2 emissions reductions, is shown below in Table 4-14:

41

Table 4-14: Cost Effectiveness of Building Block 3 

 Average Annual Cost (Billion $) Average CO2 Emissions (Million 

Short Tons) 

Avoided 

Cost per 

Short 

Ton 

Year Base Building 

Block 3 

Incremental

Cost 

Base Building 

Block 3 

Reduced 

Emissions

2022-

203042

184.7 193.7 9.1 2184.8 1940.9 243.9 $37 

EPA believes the 2022-2030 average cost of $37/ton to be reasonable.43

The feasibility of these generation levels is further confirmed by the results of other industry 

projections of RE, as well as many industry studies of the technical feasibility of even higher levels

 

39 This same procedure was used to apportion the national-level building block 3 generation totals to each of the 

three interconnections. See the IPM analysis ‘BB3: Generation Assignment’ in the docket for further details. 

40 To model 1:1 replacement, incremental RE replaced affected EGU generation from levels projected by EPA IPM 

Base Case, rather than from historical levels. All other resources – non-building block 3 RE, other existing nonaffected sources, and new sources - were available to meet projected demand growth in the analysis. In contrast, and 

to reflect the flexibilities inherent in compliance, the ‘BB3: Generation Assignment’ analysis did not include 

constraints necessitating 1:1 replacement of affected EGU generation. 

41 Refer to ‘BB3: Cost-Effectiveness’ IPM analysis, available in the docket, for further detail. 

42 The cost-effectiveness calculation is based on the 2022-2030 time period, reflecting the entirety of the interim 

compliance period through achievement of the final building block 3 generation level in 2030. The results presented 

reflect a weighted average of the 2020, 2025, and 2030 run years; specifically, one calendar year from IPM’s 2020 

run year (2022), five calendar years from IPM’s 2025 run year (2023 – 2027), and three calendar years from IPM’s 

2030 run year (2028 – 2030). 

43 The wind and solar resource potential in IPM was developed with the assistance of NREL data and 

methodologies, which includes appropriate resource exclusions and constraints to recognize land uses that are 

incompatible with RE development. The level of RE deployment required by the building block 3 analyses

represents a small amount of the total resource estimates, which, together with the regional nature of the BSER, 

effectively addresses the various examples of land use incompatibilities raised by commenters. For example, in 2030 

the total onshore wind capacity projected to be developed is only 1.5% of potential capacity (153 GW out of a 

potential of 10,468 GW). Likewise, the total solar PV potential developed in the building block 3 analysis is only 

0.1% of potential (154 GW out of a potential of 194,490 GW). Consequently, the level of RE deployment associated 

with achieving building block 3 remains technically feasible and cost-effective even under conservative land use 

assumptions. 
4 – 22 

of RE penetration. The percentage of total RE generation associated with building block 3 

projected in 2025 and 2030 is shown in Table 4-15.44 As the table shows, the highest nationallevel share of generation from renewables is 20 percent. Virtually all recent studies of achievable 

penetration level of RE in the power grid are above 20 percent, for the simple reason that 20 percent 

has been shown to be feasible – as indicated by the current RE levels discussed above – and studies 

are directed at assessing the impact of higher levels. A good review of the main integration studies 

is provided in a report by the National Renewable Energy Laboratory.45 The summary table from 

this report is included as an Appendix to this TSD. The table summarizes a range of regional and 

national studies that analyze renewable penetration levels up to 50 percent. 

Table 4-15: EPA IPM Analysis of Generation Mix 

 2025 (TWh) 2030 (TWh) 

Scenario Total RE RE% Total RE RE 

EPA IPM Base 

Case 

4328 441 10 4467 472 11 

Building Block 3 

Analysis46

4326 582 13 4473 894 20 

Another important reference point for understanding the Building Block 3 generation levels is 

found in projections of future renewables by independent industry evaluations. Two example of 

such evaluations have been recently conducted by IHS Energy and by UBS Securities LLC.47 In 

each case, these projections are presented in terms of the additions of RE capacity that are expected 

to be deployed. Comparable levels of capacity for Building Block 3 are available from the IPM 

results for the base and Building Block 3 levels and are shown in Table 4-16 along with the 

projections from IHS Energy. This table shows Building Block 3 potential RE capacity additions 

are somewhat above a midpoint, but well within the range of industry expectations. 

In the UBS projections cited, UBS estimates a solar market of 90GW. This compares closely with 

the Building Block 3 projection of 93GW of total solar capacity.48 Together these comparisons 

show that the assumed generation levels are within the range of expected feasible future 

expectations of independent industry analysts. Moreover, it is important to realize that these results

 

44 For purposes of Table 4-15, RE is comprised new and existing wind, utility-scale solar, geothermal, and new 

hydropower. 

45 NREL, Relevant Studies for NERC’s Analysis of EPA’s Clean Power Plan 111(d) Compliance, Table ES-1, at 

http://www.nrel.gov/docs/fy15osti/63979.pdf. 

46 Available in the docket as IPM run ‘BB3: Cost-Effectiveness.’ 

47 HIS Energy, North American Renewables Outlook: The rise of solar and the uncertainty of policy, 2 June 2015; 

UBS Securities LLC, US Solar & Alternative Energy, Sizing Up the US Solar Market. 

48 UBS includes some distributed solar as well as utility scale. 
4 – 23 

from the IPM model are not projections of any expected future scenario of compliance with the 

final rule, but rather results designed to show the potential impact in key variables of modeling a 

potential future based on demonstrated historical performance. In this context it is also important 

to note that compliance with the final rule may be achieved with far lower overall levels of RE 

generation. In the illustrative scenario for the rate-base compliance, for example, the total 

renewable capacity additions were 95 GW in 203049, only slightly above the 87 GW in the IHS 

Energy Low case. 

Table 4-16: Total RE Capacity Additions from 2015 (GW) 

Scenario Through 2025 Through 2030 

EPA Base Case 48 62 

EPA Building Block 350 121 233 

IHS Energy -Low 64 87 

IHS Energy -Mid 126 192 

IHS Energy -High 222 352 

4.6.3 Transmission 

Compliance with the final rule is not expected to add significant transmission requirements. The 

RIA projects that the final rule will add only 20 GW of renewable generation capacity beyond the 

base case; this is a small increment that can be accommodated in the normal transmission planning 

process. Even the larger levels of new generation for Building Block 3 considered here should not 

raise concerns about the reasonableness of the costs associated with the full build out of 

renewables. There are several reasons for this: 

• Although it is reasonable to assume that some proposed renewable scenarios will require 

specific patterns of new transmission requirements,51 transmission will be required for any 

new generation under all future scenario, with or without significant additions of 

renewables. Although the location-specific nature of renewables will place different 

requirements that can add to some transmission needs, it will also mean that generation 

and flows on the grid will shift, resulting in reduced flows and constraints in some areas. 

Since the additional renewable capacity projected for Building Block three occurs over a 

15 year period, and rises to a level of only 20 percent renewables, these additions should 

be manageable in the normal planning and expenditure process for transmission. 

 

49 See the RIA, Table 3-14. 

50 Available in the docket as IPM run ‘BB3: Cost-Effectiveness.’ 

51 An example of these requirements can be found in the Eastern Interconnection Planning Collaborative (EIPC) 

Phase 2 Report: Interregional Transmission Development and Analysis for Three Stateholder Selected Scenarios 

(December, 2012). These studies have been used to analyze the feasibility of a 30 percent wind and solar scenario by 

the Eastern Renewable Generation Integration Study (ERGIS). 
4 – 24 

• EEI members alone are expected to increase transmission expenditures substantially over 

the next five years to approximately 20 billion dollars per year, nearly doubling the 

historical investment over the average of the past five years. 52 By contrast, DOE’s 

SunShot scenario, which increases utility-scale PV to 180 GW by 2030, anticipated 

spending of $60 billion through 2050. This level is approximately double the level of solar 

assumed in this building block and still amounts to less than $2 Billion per year. 

• Using the Eastern Interconnection as an example, the additional costs required for 

transmission expansion are almost all either for generation interconnection or for 

constraint relief.53 For renewable generation, many of these costs are already captured in 

the cost estimates from the IPM modeling. For example, the cost of connecting wind to 

the existing grid are reflected in the cost classes assigned to different categories of wind 

available for deployments. The IPM model considers the location of generating resources 

and the existing limits of interregional transfer of capacity in costs of developing new 

capacity and delivering it to load, so where increased interregional congestion arises from 

delivering power from new remote supply, the cost of from re-routing the flows to avoid 

congestion are captured in the total cost of modeled scenarios. 

4.6.4 Ownership and Co-Location 

The availability of co-owned RE that is proximate to affected EGUs allows owners and operators 

to increase RE and reduce CO2 emissions at their affected EGUs. In addition to the examples of 

owners and operators of affected EGUs that own RE in section V.E.4 of the preamble to the final 

rule, there are examples of owners and operators of RE and affected EGUs in which they are colocated or located close to each other (e.g., within power control areas or balancing authorities). 

EPA has observed that companies, such as NRG, NextEra, Constellation, Sempra and Exelon, 

offer examples of co-owned RE and affected EGUs that are at least within the same power control 

area. NRG’s asset map include examples, such as western Pennsylvania wind and coal plants, 

Texas wind, coal and natural gas plants and southern California solar and natural gas plants.54

NextEra Energy Resources, a subsidiary of NextEra Energy, owns solar PV in Gloucester County, 

NJ a short distance from its two Marcus Hook NGCC plant in PA, across the Delaware River. 

They also own two NGCC facilities in Texas along with more than ten wind plants.55 Constellation 

Energy has an ownership interest in a concentrating solar thermal plant in CA along with several 

 

52 Edison Electric Institute, Transmission Projects at a Glance, March 2015, Page v. 

53 The Eastern Interconnection is a key region, because it is the limiting region for purposes of setting final rule 

goals for 2030. See the EIPC study, Section 5.1, Table 5-9, where over 95 percent of the scenario costs for 

Transmission related costs arise from these two sources. 

54 NRG, Asset Map, Available at: http://maps.nrg.com/, accessed July 2015 

55 NextEra Energy Resources, Facilities Map, Available at: 

http://www.nexteraenergyresources.com/where/us_map.shtml?type=Hydro, Accessed July 2015 
4 – 25 

small coal plants.56 Sempra Energy has neighboring natural gas and solar PV projects in both 

Boulder City, NV and Arlington, AZ.57 

These owners and operators have a direct path to reducing higher-emitting generation with RE that 

is either in close proximity or within the same grid regions regardless of their organizational type 

and regardless of whether they operate in a cost-of-service framework or in a competitive, 

organized market. 

 

56 Constellation Energy, Generation Assets Portfolio, Available at: 

http://www.constellation.com/documents/constellation_energy_pdf_documents_corpcontent_1286210749525/assetmap.pdf, Accessed July 2015 

57 Sempra Energy, Our Energy Assets, Available at: http://www.sempra.com/about/our-companies/, Accessed July 

2015 
5– 1 

CHAPTER 5: CARBON CAPTURE & STORAGE

5.1 Introduction 

Another possible approach for reducing CO2 emissions from existing fossil fuel-fired steam 

generating EGUs is through the application of carbon capture and storage technology (CCS; 

sometimes also referred to as carbon capture and sequestration). In the final standards of 

performance for new fossil fuel-fired EGUs, EPA has determined that the best system of emission 

reduction (BSER) for new fossil fuel-fired steam generating units is implementation of partial CCS 

(i.e., capturing a portion of the CO2 from the emission stream – in contrast to “full CCS” which 

would involve capturing > 90% of the CO2 from the emissions). EPA has determined that that, for 

new steam generating EGUs (utility boilers and IGCC units), partial CCS has been adequately 

demonstrated; it is technically feasible; it can be implemented at reasonable costs; it provides 

meaningful emission reductions; and its implementation will serve to promote further development 

and deployment of the technology. This chapter examines the potential for implementation of CCS 

technology at existing fossil fuel-fired steam generating EGUs. 

5.2 Carbon Capture Options for Existing Fossil Fuel-fired EGUs 

In general, CO2 capture technologies applicable to existing fossil fuel-fired power generation can 

be categorized into three approaches: (1) post-combustion capture; (2) pre-combustion capture; 

and (3) oxy-combustion. Each of these is described and discussed in more detail below. 

5.2.1 Post-combustion Capture 

Post-combustion CO2 capture refers to removal of CO2 from a combustion flue gas prior to 

discharging to the atmosphere. Separating CO2 from such a gas stream can be challenging for a 

number of reasons. Because CO2 is a dilute fraction of the combustion flue gas – typically 13-15 

% in coal-fired systems and 3-4 % in natural gas-fired systems – a large volume of flue gas must 

be treated. The flue gas from typical combustion systems is usually at near atmospheric pressure. 

Therefore, most of the available capture systems rely on chemical absorption (chemisorption) 

options (e.g., using amines) that require added energy to release the captured CO2 and regenerate 

the solvent. Many of the chemical solvents require a flue gas stream that is free of or has very low 

quantities of components – such as SO2 and HCl – that can degrade the solvent. The captured CO2, 

if it is to be transported via pipeline for storage or use in enhanced oil recovery (EOR), must then 

be compressed from near atmospheric pressure to much higher pipeline pressures (about 2,000 

psia). 
5– 2 

5.2.2 Pre-combustion Capture 

Pre-combustion capture systems are applicable to fossil fuel gasification power plants (i.e., IGCC 

units) where coal or other solid fossil fuel (e.g., petroleum coke) is converted into a synthesis gas 

(or “syngas”) by applying heat under pressure in the presence of steam and limited O2. The product 

syngas contains primarily H2 and CO – and, depending on the fuel and gasification system – some 

lesser amount of CO2. The amount of CO2 in the resulting syngas stream can be increased by

“shifting” the composition via the catalytic water-gas shift (WGS) reaction. This process involves 

the catalytic reaction of steam (“water”) with CO (“gas”) to form H2 and CO2. The resulting CO2

contained in the syngas is then captured before combustion of the H2-enriched syngas for power 

generation in a combined cycle system. Contrary to the post-combustion capture flue gas, the 

IGCC syngas can contain a high volume of CO2 and is pressurized. This allows the use of physical 

absorbents (e.g., SelexolTM, Rectisol®) that require much less added energy to release the captured 

CO2 and require less compression to get to pipeline standards.

5.2.3 Oxy-combustion 

Oxy-combustion systems for CO2 capture rely on combusting coal or other fuels with relatively 

pure O2 diluted with recycled CO2 or CO2/steam mixtures. Under these conditions, the primary 

products of combustion are water and CO2, with the CO2 purified by condensing the water. 

Challenges associated with oxy-combustion include the capital cost and energy consumption for a 

cryogenic air separation unit (ASU) to produce oxygen, introduction of N2 via boiler air 

infiltration, and excess O2 in the CO2 product stream. 

5.3 CO2 Transportation and Storage 

5.3.1 CO2 Pipeline Infrastructure 

Carbon dioxide has been transported via pipelines in the U.S. for nearly 40 years. Approximately 

50 million metric tons of CO2 are transported each year through 3,600 miles of pipelines. 

Moreover, a review of the 500 largest CO2 point sources in the U.S. shows that 95 percent are 

within 50 miles of a possible geologic sequestration site, which would lower transportation costs. 

There are multiple factors that contribute to the cost of CO2 transportation via pipelines including 

but not limited to: availability and acquisition of rights-of-way for new pipelines, capital costs, 

operating costs, length and diameter of pipeline, terrain, flow rate of CO2, and the number of 

sources utilizing the pipeline. 

5.3.2 Geologic Storage 

Existing project and regulatory experience, research, and analogs (e.g. naturally existing CO2

sinks, natural gas storage, and acid gas injection), indicate that geologic sequestration is a viable 

long term CO2 storage option. The viability of geologic sequestration of CO2 is based on a 
5– 3 

demonstrated understanding of the fate of CO2 in the subsurface. Geologic storage potential for 

CO2 is widespread and available throughout the U.S. and Canada. Nearly every state in the U.S. 

has or is in close proximity to formations with carbon storage potential including vast areas 

offshore. Estimates based on DOE studies indicate that areas of the U.S. with appropriate geology 

have a storage potential of 2,300 billion to more than 20,000 billion metric tons of CO2 in deep 

saline formations, oil and gas reservoirs and un-mineable coal seams.1

 Other types of geologic 

formations such as organic rich shale and basalt may also have the ability to store CO2; and the 

DOE is currently evaluating their potential storage capacity. 

Further evidence of the widespread availability of CO2 storage reserves in the U.S. comes from 

the Department of Interior’s U.S. Geological Survey (USGS) which has recently completed a 

comprehensive evaluation of the technically accessible storage resource for carbon storage for 36 

sedimentary basins in the onshore areas and State waters of the United States.2

 The USGS 

assessment estimates a mean of 3,000 billion metric tons of subsurface CO2 storage potential 

across the United States. For comparison, this amount is 500 times the 2011 annual U.S. energyrelated CO2 emissions of 5.5 Gigatons (Gt).3

5.3.3 Enhanced Oil Recovery (EOR) 

Geologic storage options also include use of CO2 in EOR, which is the injection of fluids into a 

reservoir to increase oil production efficiency. EOR is typically conducted at a reservoir after 

production yields have decreased from primary production. EOR using CO2, sometimes referred 

to as ‘CO2 flooding’ or CO2-EOR, involves injecting CO2 into an oil reservoir to help mobilize the 

remaining oil and make it available for recovery. The crude oil and CO2 mixture is produced, and 

sent to a separator where the crude oil is separated from the gaseous hydrocarbons and CO2. The 

gaseous CO2-rich stream then is typically dehydrated, purified to remove hydrocarbons, 

recompressed, and re-injected into the oil or natural gas reservoir to further enhance recovery. 

CO2-EOR has been successfully used at many production fields throughout the U.S. to increase 

oil recovery. The oil and natural gas industry in the United States has over 40 years of experience 

of injection and monitoring of CO2 in the deep subsurface for the purposes of enhancing oil and 

natural gas production. This experience provides a strong foundation for the injection and 

monitoring technologies that will be needed for successful deployment of CCS. 

 

1

 The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition, U.S Department of Energy, Office of 

Fossil Energy, National Energy Technology Laboratory (NETL).

2

 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National 

assessment of geologic carbon dioxide storage resources – Results: U.S. Geological Survey Circular 1386, 41 p., 

http://pubs.usgs.gov/fs/2013/1386/. 

3

 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National 

assessment of geologic carbon dioxide storage resources – Summary: U.S. Geological Survey Factsheet 2013-3020, 

6p.http://pubs.usgs.gov/fs/2013/3020/.
5– 4 

5.4 Evaluation of Retrofit CCS as BSER for Existing Fossil Fuel-fired EGUs 

5.4.1 Technical Feasibility 

In evaluating partial CCS as the BSER for new fossil fuel-fired boilers and IGCC units, EPA 

determined that the technology is feasible and adequately demonstrated for new units because the 

major components of CCS – the capture, the transportation, and the storage – are all proven 

technologies that have been demonstrated at large scale. While EPA found that partial CCS is 

technically feasible for new fossil fuel-fired boilers and IGCC units, it is more difficult to make 

that determination for the entire fleet of existing fossil fuel-fired EGUs. Developers of new 

generating facilities can select a physical location that is more amenable to CCS – such as a site 

that is near an existing CO2 pipeline or an existing oil field. Existing sources do not have the 

advantage of pre-selecting an appropriate location. Some existing facilities are located in areas 

where CO2 storage is not geologically favorable and are not near an existing CO2 pipeline. 

Developers of new facilities also have the advantage of integrating the partial CCS system into the 

original design of the new facility. Integrating a retrofit CCS system into an existing facility can 

be more challenging. Some existing sources have a limited footprint and may not have the land or 

space available to add the equipment needed to implement CO2 capture, compression, and 

transportation. Integration of the existing steam system with a retrofit CCS system can be 

particularly challenging. 

Partial CCS has been demonstrated at existing EGUs. It has been demonstrated at a pilot-scale at 

Southern Company’s Plant Barry and it is being installed for large-scale demonstration at NRG’s 

WA Parish facility. SaskPower’s Boundary Dam Unit #3, a 110 MW coal-fired utility boiler in 

Saskatchewan, began operations in October 2014. The facility has retrofit post-combustion carbon 

capture technology that is capturing approximately 90 % of the CO2 from flue gas stream. All of 

these facilities are located in areas that are either near an existing oil field or in an area that is

geologically favorable for CO2 storage. Thus, at some existing facilities, the implementation of 

partial CCS may be a preferred compliance option. However, for the reasons stated above, CCS as 

a system of emission reduction would be much more easily implemented at some existing plants 

relative to others, and for some would be precluded due to limited space. Therefore, identification 

of CCS as the best system of emission reduction for existing fossil fuel fired EGUs would require 

sub-categorization of EGUs based on such factors and EPA does not have adequate information 

to appropriately perform such a sub-categorization.

5.4.2 Reasonableness of Cost 

In the proposed standard of performance for new fossil fuel-fired EGUs (79 FR 1430), EPA 

proposed that the costs to implement partial CCS (to a level to meet the proposed emission standard 

of 1,100 lb CO2/MWh-gross) were consistent with costs for other non-natural gas-fired generating 

technologies – such as nuclear, biomass and geothermal – that utilities are considering for new 
5– 5 

intermediate and base load generating capacity. EPA also noted in the proposal, that most of the 

relatively few new projects that are in the development phase are already planning to implement 

CCS; and, as a result, the standard would not have a significant impact on nationwide energy 

prices. In issuing the final NSPS for new fossil fuel-fired EGUs, EPA – in response to public 

comments and updated cost information – reevaluated the cost to implement partial CCS and 

adjusted the final emission limitation to a standard of 1,400 lb CO2/MWh-gross. 

In contrast, EPA did not propose to identify full or partial CCS as BSER for new natural gas-fired 

stationary combustion turbines noting technical challenges to implementation of CCS at NGCC 

units as compared to implementation at new solid fossil fuel-fired sources. EPA also noted that, 

because virtually all new fossil fuel-fired generating capacity is projected to use NGCC 

technology, requiring full or partial CCS would have more of an impact on the price of electricity 

than the few projected coal plants with CCS, and the number of projects would make it difficult to 

implement in the short term. 

An emission standard for existing units based on CCS (or even partial CCS) would most certainly 

have an even more significant effect on nationwide electricity prices. Importantly, the approach 

identified as the best system of emission reduction will achieve equivalent or greater emission 

reductions at a far lower cost and provide sources and states with greater flexibility to ensure 

reliability. Therefore, as stated earlier, EPA did not find partial CCS to be an appropriate 

component of the best system of emission reduction for CO2 emissions from existing fossil fuelfired EGUs. 

5.4.3 Emission Reductions and Promotion of Advanced Technology 

An emission standard for existing units based on CCS (or even partial CCS) would clearly result 

in significant emission reductions and would certainly serve to promote further deployment, 

development and improvement in the most advanced technology. However, EPA has determined 

that such an emission standard may not be feasible in a number of cases and would involve higher 

costs (and less flexibility) than the approach identified as the best system of emission reduction. 

5.4.4 CCS and CCU in State Plans 

Affected EGUs may utilize retrofit CCS technology to reduce reported stack CO2 emissions from 

the EGU.4 Affected EGUs that apply CCS under a state plan must meet the same monitoring, 

recordkeeping and reporting requirements for sequestered CO2 as new units with CCS capability 

 

4

 Addition of retrofit CCS technology should not trigger CAA section 111(b) applicability for modified or 

reconstructed sources. Pollution control projects do not trigger NSPS modifications and addition of CCS technology 

does not count toward the capital costs of reconstruction. 
5– 6 

subject to the final CAA section 111(b) rule for new EGUs.5 Specifically, the final CAA section 

111(b) rule for new sources requires that, if a new affected EGU uses CCS to meet the applicable 

CO2 emission limit, the EGU must report in accordance with 40 CFR part 98 subpart PP (Suppliers 

of Carbon Dioxide), and the captured CO2 must be injected at a facility or facilities that report in 

accordance with 40 CFR part 98 subpart RR (Geologic Sequestration of Carbon Dioxide).6,7 See 

40 CFR part 60.46Da(h)(5) and part 60.5555(d). Taken together, these requirements ensure that 

the amount of captured and sequestered CO2 will be tracked as appropriate at project- and nationallevels, and that the status of the CO2 in its sequestration site will be monitored, including air-side 

monitoring and reporting. As detailed in the preamble for the CAA section 111(b) standards for 

new EGUs, EPA is convinced that there is ample evidence that CCS is technically feasible and 

that partial CCS can be implemented at a new fossil fuel-fired steam generating EGU at a cost that 

is consistent with the cost of other dispatchable, non-NGCC generating options. 

EPA received comments suggesting that carbon capture and utilization (CCU) technologies should 

also be allowed as a CO2 emission rate adjustment measure for affected EGUs. Potential 

alternatives to storing CO2 in geologic formations are emerging and these relatively new potential 

alternatives may offer the opportunity to offset the cost of CO2 capture. For example, captured 

anthropogenic CO2 may be stored in solid carbonate materials such as precipitated calcium 

carbonate (PCC) or magnesium or calcium carbonate, bauxite residue carbonation, and certain 

types of cement through mineralization. The carbonate materials produced can be tailored to 

optimize performance in specific industrial and commercial applications. For example, these 

carbonate materials have been used in the construction industry and, more recently and 

innovatively, in cement production processes to replace Portland cement. 

The Skyonics Skymine® project, which opened its demonstration project in October 2014, is an 

example of captured CO2 being used in the production of carbonate products. This plant converts 

CO2 into commercial products. It captures over 75,000 tons of CO2 annually from a San Antonio, 

Texas, cement plant and converts the CO2 into other products including sodium carbonate and

sodium bicarbonate.8 Other companies – including Calera9 and New Sky10 – also offer 

commercially available technology for the beneficial use of captured CO2. These processes can be 

 

5

 Standards of Performance for Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary 

Sources: Electric Utility Generating Units.

6

 The final CAA section 111(b) rule finalizes amendments to subpart PP reporting requirements, specifically 

requiring that the following pieces of information be reported: (1) the electronic GHG Reporting Tool identification 

(e-GGRT ID) of the EGU facility from which CO2 was captured, and (2) the e-GGRT ID(s) for, and mass of CO2

transferred to, each GS site reporting under subpart RR. As noted, the final 111(b) rule also requires that any 

affected EGU unit that captures CO2 to meet the applicable emissions limit must transfer the captured CO2 to a 

facility that reports under 40 CFR part 98 subpart RR. 

7

 Under final requirements in the CAA 111(b) NSPS, any well receiving CO2 captured from an affected source, be it 

a Class VI or Class II well, must report under subpart RR. A UIC Class II well’s regulatory status does not change 

because it receives such CO2, nor does it change by virtue of reporting under subpart RR.

8

 http://skyonic.com/technologies/skymine. 

9

 http://www.calera.com/beneficial-reuse-of-co2/process.html.

10 http://www.newskyenergy.com/index.php/products/carboncycle.
5– 7 

utilized in a variety of industrial applications – including at fossil fuel-fired power plants. 

However, consideration of how these emerging alternatives could be used to meet CO2 emission 

performance rates or state CO2 emission goals would require a better understanding of the ultimate 

fate of the captured CO2 and the degree to which the method permanently isolates the captured 

CO2 or displaces other CO2 emissions from the atmosphere. 

Several commenters also suggested that algae-based CCU (i.e., the use of algae to convert captured 

CO2 to useful products – especially biofuels) should be recognized for its potential to reduce 

emissions from existing fossil-fueled EGUs. 

Unlike geologic sequestration, there are currently no uniform monitoring and reporting 

mechanisms to demonstrate that these alternative end uses of captured CO2 result in overall 

reductions of CO2 emissions to the atmosphere. As these alternative technologies are developed, 

EPA is committed to work collaboratively with stakeholders to evaluate the efficacy of alternative 

utilization technologies, to address any regulatory hurdles, and to develop appropriate monitoring 

and reporting protocols to demonstrate CO2 reductions. 

In the meantime, EPA has specified that state plans may allow affected EGUs to use qualifying 

CCU technologies to reduce CO2 emissions that are subject to an emission standard, or those that 

are counted when demonstrating achievement of the CO2 emission performance rates or a state 

rate-based or mass-based CO2 emission. Approvable state plans must include analysis supporting 

how the proposed qualifying CCU technology results in CO2 emission mitigation and provide 

monitoring, reporting, and verification requirements to demonstrate the reductions. EPA would 

then review the appropriateness and basis for the analysis and the verification requirements in the 

course of its review of the state plan.